- Open Access
- Authors : Dr. Ashish Kr. Chakraverti, Sugandha Chakraverti, Dr. Yashpal Singh
- Paper ID : IJERTCONV8IS10001
- Volume & Issue : ENCADEMS – 2020 (Volume 8 – Issue 10)
- Published (First Online): 18-07-2020
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
Learning How to Learn: Meta Learning Approach to Improve Deep Learning
Dr. Ashish Kr. Chakraverti1 Sugandha Chakraverti2
Associate Professor, CSE, MIET, Gr. Noida UP Assistant Professor, CSE, RKGIT, Ghaziabad
Dr. Yashpal Singp
Professor, MIET Gr. Noida UP
AbstractMeta-Learning describes the abstraction to designing more elevated level components associated with preparing Deep Neural Networks. The expression "Meta- Learning" is tossed around in Deep Learning writing often referencing "AutoML", "Few-Shot Learning", or "Neural Architecture Search" when in reference to the robotized design of neural system architectures. Rising up out of entertainingly titled papers such as "Figuring out how to learn by inclination descent by slope descent", the success of OpenAI's rubik's solid shape mechanical hand demonstrates the development of the thought. Meta-Learning is the most promising worldview to propel the state-of-the-craft of Deep Learning and Artificial Intelligence.
Meta-learning is one of the most dynamic regions of research in the profound learning space. A few ways of thinking inside the Artificial Intelligence(AI) people group buy in to the postulation that meta-learning is one of the venturing stones towards opening Artificial General Intelligence(AGI). As of late, we have seen a blast in innovative work of meta-learning systems. In any case, a portion of the essential thoughts behind meta-learning are still generally misconstrued by information researchers and designers. From that point of view, we figured it may be a smart thought to audit a portion of the crucial ideas and history of meta-learning just as a portion of the mainstream calculations in the space.
Keywords Deep Learning; Meta learning;Artificial General Intelligence
The term metalearning first happened in the territory of instructive brain research. One of the most refered to scientists right now, Biggs, portrayed met-alearning as monitoring and assuming responsibility for one's own learning . Thus, metalearning is seen as a comprehension and adjustment of learning itself on a more elevated level than just securing subject information. In that manner, an individual mindful and equipped for metalearning can survey their learning approach and change it as per the prerequisites of a particular undertaking.
Metalearning as utilized in an AI setting has numerous likenesses to this portrayal. Subject information converts into base-realizing, where experience is gathered for one explicit learning task. Metalearning begins at a more significant level and is worried about collecting experience more than a few utilizations of a learning framework as per .
Over the most recent 20 years, AI look into was confronted with an expanding number of accessible calculations including a huge number of parametrisation, pre- preparing and postprocessing approaches just as a significantly stretched out scope of uses because of expanding registering power and more extensive availabil-ity of PC discernible informational collections. By advancing a superior comprehension of AI itself, metalearning can give a priceless assistance maintaining a strategic distance from broad experimentation techniques for calculation choice, and beast power looks for appropriate parametrisation. Seeing how to benefit from past ex-perience of a prescient model on specific undertakings can improve the presentation of a learning calculation and permit to all the more likely comprehend what causes an offered calculation to perform well on a given issue.
The possibility of metalearning isn't new, one of the first and original contri-butions having been given by . In any case, the strict term just began showing up in AI writing during the 1990s, yet still numerous publi-cations manage issues identified with metalearning without utilizing the real word. This commitment attempts to get a handle on each perspective metalearning has been examined from, refering to books, research and survey papers of the most recent decade. We trust this review will give a valuable asset to the information mining and AI people group.
The rest of this paper is composed as follows. In Section 2 we re-see meanings of metalearning given in logical writing, concentrating on com-mon topics happening in every one of them. Segment 3 portrays various ideas of metalearning, connecting them to the definitions given in Section 2. In Section4 commonsense contemplations emerging when planning a metalearning framework are talked about, while open research headings are recorded in Section 5.
First, In the 1990s, the term metalearning started to appear in machine learning re- search, although the concept itself dates back to the mid-1970s . A number of definitions of metalearning have been given, the following list cites the main review papers and books from the last decade:
Metalearning studies how learning systems can increase in efficiency through experience; the goal is to
understand how learning itself can become flexible according to the domain or task under study. ()
The primary goal of metalearning is the understanding of the inter- action between the mechanism of learning and the concrete contexts in which that mechanism is applicable. ()
Metalearning is the study of principled methods that exploit meta- knowledge to obtain efficient models and solutions by adapting ma- chine learning and data mining processes. ()
Metalearning monitors the automatic learning process itself, in the context of the learning problems it encounters, and tries to adapt its behaviour to perform better. ()
Learning systems that adapt and improve by experience are a key concept of definitions 1, 3 and 4. This in itself however does not suffice as a descrip- tion, as it basically applies to all machine learning algorithms. Metalearning becomes metalearning by looking at different problems, domains, tasks or con- texts or simply past experience. This aspect is inherent in all of the definitions, although somewhat disguised in definition 3 using the term metaknowledge in- stead. Metaknowledge as described by the authors stands for knowledge to be exploited from past learning tasks, which may both mean past learning tasks on the same data or using data of another problem domain. Definition
differs in emphasising a better comprehension of the interaction between domains and learning mechanisms, which does not necessarily imply the goal of improved learning systems, but the pursuit of a better understanding of for which tasks individual learners succeed or fail.
Rephrasing, the common ground the above definitions share, we propose to define a metalearning system as follows:
A metalearning system must include a learning subsystem, which adapts with experience.
Experience is gained by exploiting metaknowledge extracted
. . . in a previous learning episode on a single dataset, and/or
. . . from different domains or problems.
Furthermore, a concept often used in metalearning is that of a bias, which, in this context, refers to a set of assumptions influencing the choice of hypotheses for explaining the data.
distinguishes declarative bias specifying the rep- resentation of the space of hypotheses (for example representing hypotheses using neural networks only) and procedural bias, which affects the ordering of the hypothese (for example preferring hypothesis with smaller runtime). The bias in base-learning according to this theory is fixed, whereas metalearning tries to choose the right bias dynamically.
Notions of Metalearning
Metalearning can be employed in a variety of settings, with a certain disagree- ment in literature about what exactly constitutes a metalearning problem. Different notions will be presented in this section while keeping an eye on the question if they can be called metalearning approaches according to Def- inition 1. Figure 1 groups general machine and metalearning approaches in relation to
Definition 1. Each of the three circles presents a cornerstone of the definition (1: adapt with experience, 2a: meta- knowledge on same data set, 2b: meta-knowledge from different domains), the approaches are arranged into the circles and their overlapping sections depending on which parts of the defini- tion applies to them. As an example, ensemble methods do generally work with experience gained with the same data set (definition 2a) and adapt with experience (definition 1), however, the only approach potentially applying all three parts of the definition is algorithm selection, which appears where all three circles overlap.
Fig. 1 Notions of metalearning vs. components of a metalearning system
Ensemble methods and combinations of base-learners Model combination is often used when several
applicable algorithms for a problem are available. Instead of selecting a single algorithm for a problem, the risk of choosing the wrong one can be reduced by combining all or a subset of the available outcomes. In machine learning, advanced model combination can be facilitated by ensemble learning according to  and , which comprises strategies for training and combining outputs of a number of machine learning algorithms. One often used approach of this type is resampling, leading to a number of ensemble generation techniques. Two very popular resampling- based ensemble building methods are:
Bagging introduced in , which denotes repeated random sampling with replacement to produce a dataset of the same size as the original training set. The dataset is subsequently used for training of a base model and the
collection of models obtained in this way forms an ensemble with indi- vidual models decisions combined typically using voting (in classification problems) or averaging (in regression problems).
Boosting proposed in , which manipulates the probability with which samples are drawn from the original training data, to sequentially train classifiers focusing on the difficult parts of the training set. Hence each consecutive ensemble member focuses on the training examples that can- not be successfully handled by the ensemble developed up to that point. The ensemble is usually built until a specified number of ensemble mem- bers is generated (although other stopping criteria are possible) and their decisions are combined using a weighted voting mechanism. Although the ensemble members can be weak learners (i.e. models only slightly better than chance), this property must hold in the
context of an increasingly difficult resampled dataset. As a result at some stage the weak learner may in fact need to be quite complex and powerful.
The above approaches exploit variation in the data and are referred to as met- alearning methods in  and . Bagging however does not satisfy point 2 of Definition 1, as consecutive random samples from the original dataset are independent from each other, so there is no experience from previous learn- ing episodes involved. In the case of boosting however, the ensemble is built sequentially and it is the performance of previous ensemble members (i.e. ex- perience gained while trying to solve the problem) that influences the sampling process.
More often, the following two approaches are considered as metalearning techniques:
Stacked generalisation (or stacking) as introduced in , where a number of base learners is trained on the same dataset. Their outputs are subse- quently being used for a higher level learning problem, building a model linking the outcomes of the base learners to the target value. The meta- model then produces the final target outcome.
Cascade generalisation , which works sequentially. When building a model, the output of the first base learner is appended to the original feature set and passed on to the next learner with the original target values. This process can then be repeated.
Although in these cases the information about base- learning is drawn in the sense of point 2a of Definition 1, these algorithms are limited to a single problem domain with a bias that is fixed a priori, so that they, using the definition above, do not undoubtedly qualify as metalearning methods.
A considerable amount of metalearning research has been devoted to the area of algorithm recommendation. In this special case of metalearning, the aspect of interest is the relationship between data characteristics 1 and algorithm per- formance, with the final goal of predicting an algorithm or a set of algorithms suitable for a specific problem under study. As a motivation, the fact that it is infeasible to examine all possible alternatives of algorithms in a trial and error procedure is often given along with the experts necessary if pre-selection of algorithms is to take place. This application of metalearning can thus be both useful for providing a recommendation to an end-user or automatically selecting or weighting algorithms that are most promising. points out another aspect: it is not only the algorithms themselves, but different parameter settings that will naturally let performance of the same algorithm vary on different datasets. It would be possible to regard versions of the same algorithm with different parameter settings as different learning algo- rithms altogether, but the author advocates treating the subject and studying its effects differently. Such an approach has for example been taken in  and , where the authors discuss a hybrid metalearning and search based tech- nique to facilitate the choice of optimal parameter values of a Support Vector Machine (SVM). In this approach, the candidate parameter settings recom- mended by a metalearning algorithm are used a starting point for further optimization using Tabu Search or Particle Swarm
Optimization techniques, with great success.  investigate increasing the accuracy and decreasing runtime of a genetic algorithm for selecting learning parameters for a Support Vector Machine and a Random Forests classifier. Based on past experience on other datasets and corresponding dataset characteristics, metalearning is used to select a promising initial population for the genetic algorithm, reducing the number of iterations needed to find accurate solutions.
An interesting treatment of the above problem can also be found in , where the authors propose to take into account not only the expected per- formance of the algorithm but also its estimated training time. In this way the algorithms can be ordered according to the estimated training complexity, which allows to produce relatively well-performing models very quickly and then look for better solutions, while the ones already trained are producing predictions. These ideas are further extended in , where some modifications of the complexity measures used are introduced.
The classic application area of algorithm selection in machine learning is classification.  however tries to generalise the concepts to other areas including regression, sorting, constraint satisfaction and optimisation. Met- alearning for algorithm selection has also been investigated in the area of time series forecasting, where the term was first used in . A comprehensive and recent treatment of the subject can be foundin  and , where time series are clustered according to their characteristics and recommendation rules or combination weights derived with machine learning algorithms. Maintaining the Integrity of the Specifications
CONSIDERATIONS FOR USING METALEARNING
Before applying metalearning to any problem, certain practical choices have to be made. This includes the choice of a metalearning algorithm, which can even constitute a meta-metalearning problem itself. Selection of appropriate metaknowledge and the problem of setting up and maintaining metadatabases have to be tackled, research efforts of which will be summarised in this section.
As also elaborated on in , metalearning can not be seen as a magic cure to machine learning problems for a variety of reasons. First of all, the extracted metafeatures need to be representative of their problem domain, otherwise, an algorithm will fail to identify similar domains. On the same note, if a problem has not been seen before, metalearning will be unable to exploit past knowledge to improve prediction performance. Performance estimation may be unreliable because of the natural limitations of estimating the true performance of the dataset. Different metafeatures might be applicable to each dataset. These is- sues emphasise the importance of being critical when designing a metalearning system.
Metalearning algorithms gives a survey on efforts to describe properties of algorithms. The au- thor distinguishes qualitative properties (for example type of data that can be handled, learning strategy, incrementality) and quantitative properties (bias- variance profile, runtime properties like scalability and
resilience). In an effort to find an implementation and vendor-independent method for representing machine learning models, the XML-based standard PMML has been devel- oped and gained some recognition in the last years. A detailed description of PMML can be found in .
The choice of a metalearning algorithm naturally depends on the prob- lem and the task to be solved. Generally, traditional classification algorithms are very successful in metalearning algorithm selection and can include meta- decision trees , neural networks, Support Vector Machines or any other classification algorithms, with the k
-Nearest Neighbours being another pop- ular choice . Applying regression algorithms is less popular, even smaller is the number of available algorithms to learn rankings. One of the simplest ranking method involves dividing the problem space using clustering of avail- able datasets according to a distance measure (usually k -Nearest Neighbour) of the metafeatures and using average performance ranks of the cluster into which a new problem falls .  also look at the magnitude and significance of the differences in performance. The NOEMON approach introduced by  builds classifiers for each pair of base forecasting methods with a ranking be- ing generated using the classifiers outputs.  build decision trees using the positions in a ranking as target values.
According to , metaknowledge is derived in the course of employing a learn- ing system. A very common form of metaknowledge is the performance of algorithms in certain problem domains, which is to be linked with charac- teristics of the task. Several possibilities for characterising a problem domain exist.
The most straightforward form of metaknowledge extracted from the data include statistical or information- theoretic features. For classification prob- lems,  mention the number of classes and features, ratio of examples to fea- tures, degree of correlation between features and target concept and average class entropy. For other application areas, features can look completely differ- ent, as for example summarised in  for the area of time series forecasting, where features can include, for example, length, seasonality, autocorrelation, standard deviation and trends of the series. propose measures for the difficulty of a classification problem that can be used as an input for metalearning. They include class variation, denoting the probability that, by means of a distance measure, any two neighbouring data records have a different class value and example cohesiveness, measuring the density of the example distribution in the training set. In a similar approach,  also suggest comparing observations with each other and extract case base properties, which assess the quality of a dataset using measures such as redundancy, for example induced by data records that are exactly the same, or incoherency, which, for example occurs if data records have the same features but different class labels.
Alternatively to looking at the data only, information of individual algo- rithms and how they solved the problem can be considered, for example their predicted confidence intervals. This can be achieved by using a model that is fast to build and train and investigating its properties. In this spirit, sug- gest building a decision tree for a classification problem and using properties of the tree such as nodes per feature, tree depth or shape to characterise it. Another approach is landmarking as proposed in , using the performance of simple algorithms to describe a problem and correlating this information with the performance of more advanced learning algorithms. A list of land- marking algorithms can be found in . Landmarking algorithms can also be run on only a small sample of the data available, reducing the training time required. Performance information of different algorithms and learning curves generated when more data is added to the training set can then be used to select an algorithm according to .
Empirical evaluation of different categories of metafeatures in the con- text of their suitability for predicting classification accuracies of a number of standard classifiers can be found in . The authors distinguish 5 such categories of features i.e. simple, statistical, information- theoretic, landmark- ing and model-based, which corresponds to the general categorization evident from the literature.
As with any learning problem, metalearning is subject to the curse of dimensionality  and other issues, which can traditionally be solved by se- lecting a subset of relevant features. Although to the best of our knowledge, in the context of metalearning this issue has only been addressed in relatively few publications (e.g. [59, 34, 52]), we assume that the reason for this is quite simple meta-feature selection does not differ from feature selection at the base-level, and the machine learning literature is very rich in this regard (a comprehensive review of various feature selection techniques can be found in ).
As metalearning profits from knowledge obtained while looking at data from other problem domains, having sufficient datasets at ones disposal is impor- tant.  propose transforming existing datasets (datasetoids) to obtain a larger number of them and show success of the approach on a metalearn- ing post-processing problem.  states that there is no lack of experiments being done, but datasets and information obtained often remain in peoples heads and labs. He proposes a framework to export experiments to specifically designed experiment databases based on an ontology for experimentation in machine learning. The resulting database can then, for example, give informa- tion on rankings of learning algorithms, the behaviour of ensemble methods, learning curve analyses and the bias-variance behaviour of algorithms. One example of such database can be The Open Experiment Database 4 . An analysis of this database together with a critical review can be found in .
An alternative approach to the problem of scarcity metadatabases has been presented in , where the authors describe a dataset generator able t pro- duce synthetic datasets with specified values of some metafeatures (like kur- tosis and skewness). Although the proposed generator appears to be at a very early stage of development, the idea is definitely very promising, also from the point of view of performing controlled experiments on datasets with spec- ified properties. Similarly to feature selection, synthetic data generation has received a considerable attention in the recent
generic machine learning and data mining literature, especially in the context of data streams and concept drift (please see  and references therein).
CONCLUSIONS AND RESEARCH CHALLENGES Research in the area of metalearning is continuing in several directions. One area is the identification of metafeatures. As mentioned before, the vast majority of publications investigates extracting features from the dataset, mostly in the form of statistical or information theoretic measures. Landmarking is a different approach using simple base learning algorithms and their performance to describe the dataset at hand. However,  argue that characteristics of learning algorithms and gaining a better understanding of their behaviour would be a valuable research avenue with very few publications, for example , that exist in this area
A lot of publications on metalearning focus on selecting the base-learning method that is most likely to perform well for a specific problem. Fewer pub- lications like  and
consider ranking algorithms, which can be used to guide combination weights and to increase robustness of a metalearning system.
Regarding adaptivity and continuous monitoring, many approaches go fur- ther than the static traditional metalearning approaches, for example by using architectures that support life-long learning such as in . However, research in this area can still go a long way further investigating continuous adjust- ment, rebuilding or discarding of base-learners with the help of metalearning approaches.
Users of predictive systems are faced with a difficult choice of an ever in- creasing number of models and techniques. Metalearning can help to reduce the amount of experimentation by providing dynamic advice in form of assistants, decrease the time that has to be spent on introducing, tuning and maintaining models and help to promote machine learning outside of an academic environment.
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