 Open Access
 Total Downloads : 84
 Authors : Ms. M. Indira , Dr. S. Jayasankari
 Paper ID : IJERTV8IS090204
 Volume & Issue : Volume 08, Issue 09 (September 2019)
 Published (First Online): 01102019
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
SrensenDice Cuckoo Feature Selection based Gaussian Neuro Fuzzy Classification for Improved Agriculture Seed Growth
Ms. M. Indira Dr. S. Jayasankari
Assistant Professor, Associate Professor,
Department of Computer Science, Department of Computer Science,

Arts College for Women (Autonomous), P.K.R. Arts College for Women (Autonomous), Gobichettipalayam, India. Gobichettipalayam, India.
Abstract – Seed classification is a significant issue to be solved in data mining to increase the agriculture growth. Few research works are designed for classifying seed as normal or abnormal using different techniques. However, accuracy of seed classification was not sufficient. Further, time taken to classify the seed data was more. In order to overcome such limitations, a SÃ¸rensenDice Cuckoo Feature Selection Based Gaussian Neuro Fuzzy Classifier (SCFSGNFC) Technique is proposed. SCFSGNFC Technique is designed to classify the seed data as normal or abnormal with higher classification accuracy and minimal feature selection time and thereby enhancing agriculture growth. The SCFSGNFC Technique initially takes number of seed data and their features as input. After that, SÃ¸rensenDice Similarity Based Cuckoo Feature Selection (SSCFA) algorithm is introduced to choose relevant features from the large volume of data with lower time. By using the selected significant features, the seed classification is performed in SCFSGNFC Technique with application of Gaussian Neurofuzzy classifier. During the classification process, SCFSGNFC Technique employs Gaussian membership function and fuzzy ifthen rules to precisely classify the each input data as normal or abnormal class with a minimal time. This helps for SCFSGNFC Technique to improve the classification performance of seed data categorization with a lower error rate. The experimental evaluation of SCFSGNFC Technique is carried out using soybean dataset on factors such as feature selection time, classification accuracy and error rate with respect to number of features and seed data. The experimental result shows that the SCFSGNFC Technique is able to increase the classification accuracy and also reduces the feature selection time of seed classification in agriculture field as compared to state oftheart works.
KeywordsAgriculture Dataset, Fitness Function, Gaussian Neurofuzzy classifier,Relevant Features, Seed Data, SÃ¸rensen Dice Similarity Based Cuckoo Feature Selection

INTRODUCTION
In agriculture field, seed plays a vital role. The classification of seeds as the normal seed or defected is challenging issue in data mining. In this advanced era, seed classification plays major role to improve the agricultural growth. Most of the recent research focused on classification of seeds using different data mining techniques. But, the classification accuracy was not improved. In order to improve the seed classification performance with a minimal time, SCFSGNFC Technique is introduced in this research work by using SÃ¸rensenDice Similarity Based Cuckoo Feature Selection (SSCFA) algorithm and Gaussian NeuroFuzzy (GNF) Classifier.
Multilayer perceptron network classifier was introduced in [1] for classifying highquality seeds from lowquality seeds. However, accuracy of Multilayer perceptron network classifier was not enough. A Hybrid Kernel based Support Vector Machine (HSVM) was designed in [2] for categorizing the multiclass agricultural data with attributes. But, the feature selection was not carried out that resulted in maximal error rate.
The Cband, dual polarimetric and temporal satellite of RISAT1 was discussed in [3]. But, the error rate was not reduced by using the divergence method. Decisionmaking tool was introduced in [4] for choosing the best crop in a given agricultural land with enhanced accuracy. However, time complexity was very higher.
A comparative result analysis were carried out in
[5] for postharvest growth discovery using geometric features. However, the classification time was not reduced due to the comparative analysis of classification process. The survey of diverse data mining techniques designed for analysis of agriculture data and thereby enhancing the crop production was presented in [6].Hybrid rough fuzzy soft classifier was presented in [7] for agriculture crop selection with a lower time. However, error rate during classification process was higher. A hybrid MultiCriteria decision making technique was developed in [8] for improving the diseases diagnosis performance in plants. But, feature selection was not performed in this technique.
Machine Learning Approach was designed in [9] to find proper crops according to climatic conditions and
thereby maximize yield rate. But, classification accuracy was lower. A novel diagnostic method was introduced in

for predicting fungal pathogens on vegetable seeds. But, computational time taken for diagnosis was more.
In order to addresses the above mentioned existing issues, SCFSGNFC Technique is introduced in this research work. The key contributions of SCFSGNFC Technique is described in below,

To increase the feature selection performance as compared to stateoftheart works, SÃ¸rensenDice Similarity Based Cuckoo Feature Selection (SSCFA) algorithm is proposed in SCFSGNFC Technique as it provides robust solution for optimization problems
during feature selection process. Because, proposed SS CFA contains many advantages for example easy implementation, stable convergence characteristic and good computational efficiency on the contrary to conventional optimization algorithms. The designed SS CFA algorithm identifies optimal features from an input dataset for efficient seeds classification with enhanced accuracy.

To improve the seed classification performance in agricultural field as compared to conventional works, Gaussian NeuroFuzzy (GNF) Classifier is proposed in
SCFSGNFC Technique. The advantages of neural networks and fuzzy systems are combined as a neuro fuzzy approach in GNF Classifier. GNF Classifier is a fuzzy network that contains a fuzzy inference system to solve some drawbacks of neural networks and fuzzy systems. Because, GNF Classifier in proposed SCFS GNFC Technique can learns and represents knowledge in an interpretable way and learning ability. This supports for SCFSGNFC Technique to exactly classify the seed data as normal or abnormal with a lower time.
The rest of paper is constructed as follows. Section 2 portrays the related works. Section 3 presents the exhaustive process of proposed SCFSGNFC Technique. In section 4, an experimental setting of proposed technique is demonstrated. Section 5 provides the results and discussion of certain parameters. Finally, the conclusion of the research work is depicted in Section 6.



RELATED WORKS
A fuzzybased multicriteria decisionmaking was developed in [11] to discover the crop pattern. A novel method was designed in [12] to offer an effective classification of the soybean seed vigor.
A realtime, noninvasive, microoptrode technique was introduced in [13] for determining the seed viability. A seed yield evaluation modeling using classification and regression trees (CART) was presented in [14].
A hybrid ensemble approach was designed in [15] for solving multiclass classification issues. But, the classification time was not reduced. In [16], the differentiations in seed germination and seed growth were evaluated at intra and interprovenance levels.
A novel predictive model was presented in [17]to find seed classes with application of machine learning
algorithms to attain high crop production. A back propagation (BP) neural network was applied in [18] to find out the seed distribution.
An analysis and impact factors on agriculture field using different data mining techniques were presented in [19]. Rough set and decision tree ensemble was employed in [20] to enhance the detection performance of agricultural data.
Based on the above existing techniques, a novel seed classification technique called SCFSGNFC Technique is developed which detailed described in below section.

SÃ˜RENSENDICE CUCKOO FEATURE SELECTION BASED GAUSSIAN NEURO FUZZY
CLASSIFIER TECHNIQUE
SÃ¸rensenDice Cuckoo Feature Selection Based Gaussian Neuro Fuzzy Classifier (SCFSGNFC) Technique is proposed in order to improve the performance of seed classification for predicting the agriculture growth. The SCFSGNFC Technique is designed by combining the SÃ¸rensenDice Similarity Based Cuckoo Feature Selection (SSCFA) algorithm and Gaussian NeuroFuzzy (GNF) Classifier on the contrary to conventional works. Therefore, proposed SCFSGNFC Technique gives best classification result for identifying the seed growth in the agriculture field as compared to existing algorithm. The architecture diagram of SCFSGNFC Technique is shown in below Figure 1.
Agricul
ture
Number of
Agricul
ture
Number of
Input
SÃ¸rensenDice Similarity Based Cuckoo Feature
Find
significant
Input
SÃ¸rensenDice Similarity Based Cuckoo Feature
Find
significant
Gaussian NeuroFuzzy Classifier
Classify seed data as normal
or abnormal
Gaussian NeuroFuzzy Classifier
Classify seed data as normal
or abnormal
Efficient classification of Seeds in Agricultural field
Efficient classification of Seeds in Agricultural field
Figure 1 Architecture Diagram of SCFSGNFC Technique for Predicting Agriculture Seed Growth
Figure 1 shows the overall process of SCFSGNFC Technique to achieve enhanced classification accuracy for finding seed quality. As presented in above figure, SCFS GNFC Technique initially takes agriculture dataset i.e. Soybean Dataset as input. After getting input, SCFSGNFC Technique carried out SÃ¸rensenDice Similarity Based Cuckoo Feature Selection process where features that are more related for seed classification are selected with
minimal time. After feature selection, SCFSGNFC Technique applies Gaussian NeuroFuzzy Classifier that efficiently classifies each input seed data as normal or abnormal by using selected features with a lower amount of time. Thus, SCFSGNFC Technique improves the seed classification performance as compared to stateoftheart works. The detailed processes of SCFSGNFC Technique are described in below subsection.

SÃ¸rensenDice Similarity Based Cuckoo Feature Selection
The SÃ¸rensenDice Similarity Based Cuckoo Feature Selection (SSCFA) algorithm is designed to choose the features that are more imperative for classifying seeds in agriculture field. On the contrary to conventional works, SSCFA algorithm is proposed with application of SÃ¸rensenDice similarity measurement in cuckoo search optimization. The SSCFA is an optimization algorithm which depends on the obligate brood parasitism of cuckoo species by putting their eggs in the nests of other host birds. Each egg in a nest denotes a solution (i.e. best features for seed categorization), and a cuckoo egg indicates a new solution. The aim of SSCFA algorithm is to find optimal solutions (i.e. relevant features) for seed classification by replacing a notsogood solution (i.e. irrelevant features) in the nests (i.e. input agriculture dataset). The SSCFA algorithm depends on three idealized rules explained in below,

Each cuckoo puts one egg at a time, and leaves its egg in an arbitrarily selected nest.

The best nests with high quality eggs are considered for the next generation.

The number of hosts nests is constant, and the egg laid by a cuckoo is identified by the host bird with a probability.
Based on the above rules, SSCFA algorithm selects the more relevant features for improving the seed classification accuracy. The process of SSCFA algorithm is depicted in below Figure 2.
Initialize populations with seed features
Initialize populations with seed features
Agricultu re dataset
Define objective function
Define objective function
Input
Compute fitness function
Compute fitness function
Rank features based on fitness
Find current best features
Find current best features
Select current best features as more relevant
Figure 2 Processes of SSCFA Algorithm for Feature Selection
Figure 2 demonstrates the flow processes of SS CFA Algorithm to improve the feature selection performance with a minimal time complexity. As presented in above figure, SSCFA Algorithm first get agriculture dataset i.e. Soybean Dataset as input which includes many number of features represented as . Here, denotes the total number of features available in input dataset. Then SSCFA Algorithm initialize the population of n hosts nests with seed features. Followed by, SSCFA Algorithm define the objective function using below expression,
(1)
Here, objective function is to choose the feature with higher similarity value for seeds classification whereas denotes the SÃ¸rensenDice similarity value
of feature and helps to find the features with higher similarity value. Then, fitness of the each feature is computed as follows,
(2)
From the above equation (2), denotes fitness
function of feature whereas indicates similarity value. On the contrary to existing works, the SÃ¸rensen Dice Indexing is applied in SSCFA Algorithm for determining the similarity between the features using below,
(3)
(4)
The above equation (4) is substituted to the equation (3) to acquire final similarity results,
(5)
From equation (5), represents a SÃ¸rensen Dice similarity value, and denotes two features in
the input dataset and indicates the ratio of mutual
dependence between two features. The intersection symbol represents a mutual dependence and and denotes the cardinalities of the two features. The SÃ¸rensen Dice similarity value is ranges between 0 and 1. If the two features are similar, then the output value is 1. When the two features are dissimilar, the output value is 0.
After determining the fitness value, the current best solutions (i.e. relevant features) are selected by assigning the rank. The SSCFA Algorithm assigns rank to each feature based on their fitness value using below mathematical formulation,
(6)
From equation (6), indicates a rank. Based on the rank assigned, SSCFA Algorithm chooses the current
best features (i.e. features with higher rank) for increasing the seeds classification accuracy.
The algorithmic step of SSCFA is explained in
// SÃ¸rensenDice Similarity Based Cuckoo Feature Selection Algorithm
Input: Agriculture Dataset AD; Number of Features
Output: Enhanced feature selection accuracy
Step 1:Begin
// Feature Selection
Step 2: Initialize the population with number of features
// SÃ¸rensenDice Similarity Based Cuckoo Feature Selection Algorithm
Input: Agriculture Dataset AD; Number of Features
Output: Enhanced feature selection accuracy
Step 1:Begin
// Feature Selection
Step 2: Initialize the population with number of features
below.
Step 3: For each
Step
Define
objective
Step 3: For each
Step
Define
objective
4:
4:
function
funcion
using (1)
using (1)
Step 5: Determine fitness function using (2)
Step 6: Rank based on using (6)
Step 7: Find current best
Step 8: Select current best feature as relevant to perform seed classification
Step 9: End for Step 10:End
Step 5: Determine fitness function using (2)
Step 6: Rank based on using (6)
Step 7: Find current best
Step 8: Select current best feature as relevant to perform seed classification
Step 9: End for Step 10:End
Algorithm 1 SÃ¸rensenDice Similarity Based Cuckoo Feature Selection
From equation (7), denotes the feature of seed data and indicates the fuzzy
set of the feature in the rule; and point outs label of class. Here, is determined with the help of membership function.
In GNF Classifier, the feature space is partitioned into multiple fuzzy subspaces with help of fuzzy ifthen rules. These fuzzy rules are represented by a network structure. The GNF Classifier is a multilayer feedforward network. The GNF Classifier in SCFSGNFC Technique contains input, fuzzy membership, fuzzification, de fuzzification, normalization, and output layers. Besides, GNF Classifier contains multiple inputs and multiple outputs. Figure 3 shows structure of GNF Classifier for agriculture seed classification.
Algorithm 1 depicts the step by step processes of SSCFA. By using the above algorithmic steps, SSCFA initialize the population of n hosts nests with features and consequently defines objective function. Then, SSCFA computes fitness value for each features using SÃ¸rensen Dice Similarity measurement. After that, SSCFA ranks the features according to their fitness value. Finally, SSCFA chooses the features with higher rank as current best solutions to efficiently classify the seeds. From that, SS CFA significantly selects the more significant features in input dataset with higher accuracy and minimal time consumption as compared to conventional works.


Gaussian NeuroFuzzy Classifier
Defuzzifi cation
In p
In p
Membe rship
Membe rship
Fuzzifi cation
Fuzzifi cation
Figure 3 Structure of GNF Classifier
Norm alizati
Ou tpu
The Gaussian NeuroFuzzy (GNF) Classifier is designed in SCFSGNFC Technique with aiming at enhancing classification performance of seeds. On the contrary to stateoftheart works, GNF Classifier is introduced by combining the Gaussian membership function in NeuroFuzzy classification. The GNF Classifier is designed to categorize the seed data as normal or abnormal and thereby predicting the seed growth in the agriculture field.
In GNF Classifier, fuzzy classification rule shows the relation between the input seed data features and classes which is mathematically represented as,
and and then class is (7)
Figure 3 depicts the processes of GNF Classifier to enhance the accuracy of seed classification in agriculture field where input layer get the number of seed data from soybean dataset and then forward it to membership layer.
In Membership layer, the membership function of all input seed data is discovered. On the contrary to existing works, a Gaussian membership function is used in GNF Classifier. Because, Gaussian membership function has fewer parameters and smoother partial derivatives for parameters. Thus, Gaussian membership function is mathematically obtained as,
// Gaussian NeuroFuzzy Classifier Algorithm
Input: Agriculture Dataset AD; Number of Seed Data ; Selected Relevant Features Output: Improved Classification Accuracy
Step 1:Begin
Step 2: Input layer takes as input
Step 3: For each seed data
Step 4: Compute Gaussian membership function using (8)
Step 5: Measure firing strength of rule with respect to to be classified using (9)
Step 6: Calculate weighted output for data that belongs to class using(10)
Step 7: Normalize value of input seed data that belongs to the class using (11)
Step 8: Classify seed data as normal or abnormal using (12)
Step 9: End For Step 10: End
// Gaussian NeuroFuzzy Classifier Algorithm
Input: Agriculture Dataset AD; Number of Seed Data ; Selected Relevant Features Output: Improved Classification Accuracy
Step 1:Begin
Step 2: Input layer takes as input
Step 3: For each seed data
Step 4: Compute Gaussian membership function using (8)
Step 5: Measure firing strength of rule with respect to to be classified using (9)
Step 6: Calculate weighted output for data that belongs to class using(10)
Step 7: Normalize value of input seed data that belongs to the class using (11)
Step 8: Classify seed data as normal or abnormal using (12)
Step 9: End For Step 10: End
(8)
From the above equation (8), represents the membership grade of rule and
feature whereas indicates seed data and
feature. Here, and denotes center and width of Gaussian function.
In fuzzification layer, each node produces a signal corresponding to the degree of fulfillment of the fuzzy rule
for input seed data . This represents firing strength of
a fuzzy rule with respect to a seed data to be classified. The
firing strength of the rule is mathematically
determined as,
(9)
From the above equation (9), point outs the number of seed features. In defuzzification layer, weighted outputs are measured. If a rule controls a particular class region, the weight among that rule output and the particular class is larger than the other weights. Otherwise, the class weight is lower. The weighted output for input seed data that belongs to the class is mathematically estimated as,
(10)
From the above equation (10), represents
the degree of belonging to class that is controlled by rule and indicates the number of rules.
In normalization layer, the outputs of network is normalized, because the summation of weights may be larger than 1 in some circumstance.
(11)
From the above equation (11), indicates the normalized value of the seed data that belongs to the class and is the number of classes.
Followed by, the class label for seed data is obtained by the maximum value using below expression,
(12)
From the above equation (12), represent the
class label of the seed data in input agriculture
dataset. By using the above processes, GNF Classifier classifies input seed data as normal or abnormal with higher accuracy.
The algorithmic process of Gaussian NeuroFuzzy Classifier is described in below,
Algorithm 2 Gaussian NeuroFuzzy Classifier
Algorithm 2 shows the step by step processes of GNF Classifier. By using the above algorithmic steps, GNF Classifier accurately categorizes the each input seed data
into normal or abnormal classes with a lower amount of time utilization. This helps for SCFSGNFC Technique to effectively find the seed quality and growth in agriculture field as compared to existing works.


EXPERIMENTAL SETTINGS
In order to measure the performance of proposed, SCFSGNFC Technique is implemented in Java Language using agriculture dataset i.e. Soybean Dataset. This dataset is obtained from UCI Machine Learning Repository. The Soybean Dataset contains 35 attributes and 307 instances. From these 35 attributes (i.e. features), SCFSGNFC Technique selects optimal number of features for finding seed disease through classification. The SCFSGNFC Technique takes different number of seed data in the range of 30300 from Soybean Dataset to perform experimental evaluation. The performance of SCFSGNFC Technique is measured in terms of feature selection time, classiication accuracy and error rate and compared with two existing methods [1] and [2].

RESULTS AND DISCUSSIONS
In this section, the experimental result of SCFS GNFC Technique is compared with two stateoftheart works namely Multilayer perceptron network classifier [1] and Hybrid Kernel based Support Vector Machine (H SVM) [2]. The efficiency of SCFSGNFC Technique is determined with the aid of below tables and graphs.

Performance Measure of Feature Selection Time
Feature Selection Time determines an amount of time required to select the relevant features from an input dataset. The feature selection time is measured using below mathematical expression,
(13)
From equation (13), represent time utilized for choosing single features as relevant or
irrelevant whereas denotes total number of features considered for experimental process. The feature selection time is evaluated in terms of milliseconds (ms).
Sample Mathematical Calculation for Feature Selection Time

Proposed SCFSGNFC Technique: Number of features is 5 and the time taken for selecting one feature is 0.9 ms, then the

Existing Multilayer perceptron network classifier: Number of features is 5 and the time needed for choosing one feature is 1.2 ms, then the

Existing HSVM: Number of features is 5 and the time employed for selecting one feature is 1.5 ms, then the
In order to evaluate the time complexity involved during process of feature selection for seed classification, SCFSGNFC Technique is implemented in Java language by considering varied number of features in the range of 5
35. When accomplishing the experimental evaluation using 30 features from soybean dataset, SCFSGNFC Technique attains 14 ms feature selection time whereas conventional works Multilayer perceptron network classifier [1] and H SVM [2] gets 15 ms, 17 ms respectively. From the above get experimental results, feature selection time using SCFSGNFC Technique is lower as compared to other stateoftheart works Multilayer perceptron network classifier [1] and HSVM [2]. The comparative result analysis of feature selection time is depicted in below Table 1.
TABLE 1 TABULATION FOR FEATURE SELECTION TIME
Number of features (n)
Feature Selection Time (ms)
SCFSGNFC
Multilayer perceptron network classifier
HSVM
5
5
6
8
10
6
8
10
15
8
11
12
20
10
13
14
25
12
14
15
30
14
15
17
35
15
18
20
Figure 4 Experimental Result of Feature Selection Time versus Number of Features
Figure 4 presents the impact of feature selection time with respect to diverse number of features using three
methods namely SCFSGNFC Technique, Multilayer perceptron network classifier [1] and HSVM [2]. As demonstrated in the above graphical representation, SCFS GNFC Technique provides minimal feature selection time for efficient seed classification as compared to existing Multilayer perceptron network classifier [1] and HSVM
[2] respectively. This is due to application of SÃ¸rensen Dice Similarity Based Cuckoo Feature Selection (SSCFA) algorithm in SCFSGNFC Technique on the contrary to existing algorithm. With application of SSCFA algorithm, SCFSGNFC Technique find outs the features with higher rank as current best solutions to significantly classify the seeds with a lower amount of time consumption. This supports for SCFSGNFC Technique to employ minimal time to choose the relevant features from an input dataset as compared to other conventional works. Hence, SCFS GNFC Technique reduces the feature selection time by 19% when compared to existing Multilayer perceptron network classifier [1] and 29 % when compared to existing HSVM [2].


Performance Measure of Classification Accuracy
Classification accuracy estimates the ratio of a number of seed data that are accurately classified as normal or abnormal seed to the total number of seed data. The classification accuracy is mathematically obtained as,
(14)
From equation (14), indicates number of
seed data that are exactly classified and designates a total number of seed data taken for simulation process. The classification accuracy is determined in terms of percentage (%).
Sample Mathematical Calculation for Classification Accuracy

Proposed SCFSGNFC Technique: Number of seed data is properly classified are 27 and the total number of seed data is 30. Then the CA

Existing Multilayer perceptron network classifier: Number of seed data is correctly classified is 24 and the total number of seed data is 30. Then the CA

Existing HSVM: Number of seed data is accurately classified are 22 and the total number of seed data is
30. Then the CA
For measuring the accuracy of seed classification in agriculture field, SCFSGNFC Technique is implemented in Java language with different number of seed data in the range of 30300. When performing the experimental work using 270 seed data from soybean dataset, SCFSGNFC Technique obtains 96 % classification accuracy whereas stateoftheart works Multilayer perceptron network classifier [1] and HSVM
[2] acquires 91 %, 85 % respectively. From these experimental results, classification accuracy using SCFSGNFC Technique is very higher when compared to other conventional works Multilayer perceptron network classifier [1] and HSVM [2]. The experimental result analysis of classification accuracy is portrayed in below Table 2.
TABLE 2 TABULATION FOR CLASSIFICATION ACCURACY
Number of seed data (N)
Classification accuracy (%)
SCFSGNFC
Multilayer perceptron network classifier
HSVM
30
90
80
73
60
88
83
75
90
91
87
77
120
93
83
78
150
92
84
75
180
94
85
79
210
91
85
72
240
95
89
73
270
96
91
85
300
97
93
89
Figure 5 Experimental Result of Classification Accuracy versus Number of Seed Data
Figure 5 shows the impact of classification accuracy based on various numbers of seed data using three methods namely SCFSGNFC Technique, Multilayer perceptron network classifier [1] and HSVM [2]. As depicted in the above graphical diagram, SCFSGNFC Technique provides higher accuracy for seed categorization in agriculture field when compared to existing Multilayer pereptron network classifier [1] and HSVM [2] respectively. This is because of application of Gaussian NeuroFuzzy (GNF) Classifier in SCFSGNFC Technique on the contrary to conventional algorithms. In GNF Classifier, Gaussian membership function and NeuroFuzzy classification are combined in order to enhance the performance of seed categorization. This helps for SCFSGNFC Technique to increase ratio of a number of seed data that are accurately classified as normal or abnormal when compared to other stateofthe art works. Therefore, SCFSGNFC Technique enhances the classification accuracy by 8 % when compared to existing Multilayer perceptron network classifier [1] and 20 % when compared to existing HSVM [2].


Performance Measure of Error Rate
Error rate calculates the ratio of a number of seed data that are incorrectly classified to the total number of seed data. The error rate is mathematically measured using below,
(15)
From equation (15) point outs a number of
seed data are inaccurately classified whereas N indicates a total number of seed data. The error rate is measured in terms of percentage (%).
Sample Mathematical Calculation for Error rate

Proposed SCFSGNFC Technique: Number of seed data is incorrectly classified are 3 and the total number of seed is 30. Then the

Existing Multilayer perceptron network classifier: Number of seed data is wrongly classified is 6 and the total number of seed data is 30. Then the

Existing HSVM: Number of seed data is mistakenly classified are 8 and the total number of seed data is 30. Then the
To determine the error rate involved during the process of seed classification in agriculture field, SCFS GNFC Technique is implemented in Java language with help of various number of data in the range of 30300. When conducting the experimental process using 210 seed data from soybean dataset, SCFSGNFC Technique gets 9
% error rate whereas existing works Multilayer perceptron network classifier [1] and HSVM [2] obtains 15 %, 28 % respectively. Accordingly, error rate using SCFSGNFC Technique is very lower when compared to other conventional works Multilayer perceptron network classifier [1] and HSVM [2]. The performance result analysis of error rate is demonstrated in below Table 3.
Table 3 Tabulation for Error rate
Number of seed data (N)
Error rate (%)
SCFS GNFC
Multilayer perceptron network classifier
HSVM
30
10
20
27
60
12
17
25
90
9
13
23
120
7
18
23
150
8
16
25
180
6
15
21
210
9
15
28
240
5
11
27
270
4
9
15
300
3
7
11
Figure 6 Experimental Result of Error Rate versus Number of Seed Data
selection time for attaining enhanced seed growth in agricultural field when compared to stateoftheart works.
REFERENCES
Figure 6 illustrates the impact of error rate with respect to different numbers of seed data using three methods namely SCFSGNFC Technique, Multilayer perceptron network classifier [1] and HSVM [2]. As presented in the above graphical figure, SCFSGNFC Technique provides lower error rate for accurately classify seeds as normal or abnormal in agriculture field as compared to conventional Multilayer perceptron network classifier [1] and HSVM
[2] respectively. This is owing to application of Gaussian NeuroFuzzy (GNF) Classifier in SCFSGNFC Technique on the contrary to stateoftheart algorithms. With the support of GNF Classifier algorithmic process, SCFS GNFC Technique correctly classifies each input seed data into normal or abnormal classes by using Gaussian membership function result. This assists for SCFSGNFC Technique to minimize the ratio of a number of seed data that are wrongly classified when compared to other state oftheart works. As a result, SCFSGNFC Technique reduces the error rate by 49 % when compared to existing Multilayer perceptron network classifier [1] and 68 % when compared to existing HSVM [2]. 

CONCLUSION

An effective SCFSGNFC Technique is designed with the goal of increasing the performance of seed data classification in agriculture field. The goal of SCFSGNFC Technique is achieved with the application of SSCFA algorithm and GNF Classifier. The proposed SCFSGNFC Technique attain higher accuracy and takes lower amount of time for selecting the key features from an input agriculture dataset as compared to stateoftheart works. Moreover, proposed SCFSGNFC Technique obtained enhanced performance for categorizing the seed data into corresponding classes (i.e. normal or abnormal) with a minimal amount of time consumption as compared to conventional algorithms. From that, SCFSGNFC Technique also reduces the ratio of data that are incorrectly classified as normal or abnormal for efficient seeds quality measurement as compared to existing works. The effectiveness of SCFSGNFC Technique is evaluated in terms of classification accuracy, feature selection time and error rate and compared with two existing works. The experimental result depicts that SCFSGNFC Technique provides a better performance with an improvement of classification accuracy and minimization of feature

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