Joaquín Derrac

Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study

The nearest neighbor classifier is one of the most used and well-known techniques for performing recognition tasks. It has also demonstrated itself to be one of the most useful algorithms in data mining in spite of its simplicity. However, the nearest neighbor classifier suffers from several drawbacks such as high storage requirements, low efficiency in classification response, and low noise tolerance. These weaknesses have been the subject of study for many researchers and many solutions have been proposed. Among them, one of the most promising solutions consists of reducing the data used for establishing a classification rule (training data) by means of selecting relevant prototypes. Many prototype selection methods exist in the literature and the research in this area is still advancing. Different properties could be observed in the definition of them, but no formal categorization has been established yet. This paper provides a survey of the prototype selection methods proposed in the literature from a theoretical and empirical point of view. Considering a theoretical point of view, we propose a taxonomy based on the main characteristics presented in prototype selection and we analyze their advantages and drawbacks. Empirically, we conduct an experimental study involving different sizes of data sets for measuring their performance in terms of accuracy, reduction capabilities, and runtime. The results obtained by all the methods studied have been verified by nonparametric statistical tests. Several remarks, guidelines, and recommendations are made for the use of prototype selection for nearest neighbor classification.

A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification

The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve classification and pattern recognition tasks. Despite its high classification accuracy, this rule suffers from several shortcomings in time response, noise sensitivity, and high storage requirements. These weaknesses have been tackled by many different approaches, including a good and well-known solution that we can find in the literature, which consists of the reduction of the data used for the classification rule (training data). Prototype reduction techniques can be divided into two different approaches, which are known as prototype selection and prototype generation (PG) or abstraction. The former process consists of choosing a subset of the original training data, whereas PG builds new artificial prototypes to increase the accuracy of the NN classification. In this paper, we provide a survey of PG methods specifically designed for the NN rule. From a theoretical point of view, we propose a taxonomy based on the main characteristics presented in them. Furthermore, from an empirical point of view, we conduct a wide experimental study that involves small and large datasets to measure their performance in terms of accuracy and reduction capabilities. The results are contrasted through nonparametrical statistical tests. Several remarks are made to understand which PG models are appropriate for application to different datasets.

Evolutionary-based selection of generalized instances for imbalanced classification

In supervised classification, we often encounter many real world problems in which the data do not have an equitable distribution among the different classes of the problem. In such cases, we are dealing with the so-called imbalanced data sets. One of the most used techniques to deal with this problem consists of preprocessing the data previously to the learning process. This paper proposes a method belonging to the family of the nested generalized exemplar that accomplishes learning by storing objects in Euclidean n-space. Classification of new data is performed by computing their distance to the nearest generalized exemplar. The method is optimized by the selection of the most suitable generalized exemplars based on evolutionary algorithms. An experimental analysis is carried out over a wide range of highly imbalanced data sets and uses the statistical tests suggested in the specialized literature. The results obtained show that our evolutionary proposal outperforms other classic and recent models in accuracy and requires to store a lower number of generalized examples.

Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection

In recent years, fuzzy rough set theory has emerged as a suitable tool for performing feature selection. Fuzzy rough feature selection enables us to analyze the discernibility of the attributes, highlighting the most attractive features in the construction of classifiers. However, its results can be enhanced even more if other data reduction techniques, such as instance selection, are considered. In this work, a hybrid evolutionary algorithm for data reduction, using both instance and feature selection, is presented. A global process of instance selection, carried out by a steady-state genetic algorithm, is combined with a fuzzy rough set based feature selection process, which searches for the most interesting features to enhance both the evolutionary search process and the final preprocessed data set. The experimental study, the results of which have been contrasted through nonparametric statistical tests, shows that our proposal obtains high reduction rates on training sets which greatly enhance the behavior of the nearest neighbor classifier.

Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects

In recent years, many nearest neighbor algorithms based on fuzzy sets theory have been developed. These methods form a field, known as fuzzy nearest neighbor classification, which is the source of many proposals for the enhancement of the k nearest neighbor classifier. Fuzzy sets theory and several extensions, including fuzzy rough sets, intuitionistic fuzzy sets, type-2 fuzzy sets and possibilistic theory are the foundations of these hybrid techniques, designed to tackle some of the drawbacks of the nearest neighbor rule. In this paper the most relevant approaches to fuzzy nearest neighbor classification are reviewed, as are applications and theoretical works. Several descriptive properties are defined to build a full taxonomy, which should be useful as a future reference for new developments. An experimental framework, including implementations of the methods, datasets, and a suggestion of a statistical methodology for results assessment is provided. A case of study is included, featuring a comparison of the best techniques with several state of the art crisp nearest neighbor classifiers. The work concludes with the suggestion of some open challenges and ways to improve fuzzy nearest neighbor classification as a machine learning technique.

IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule

Feature and instance selection are two effective data reduction processes which can be applied to classification tasks obtaining promising results. Although both processes are defined separately, it is possible to apply them simultaneously. This paper proposes an evolutionary model to perform feature and instance selection in nearest neighbor classification. It is based on cooperative coevolution, which has been applied to many computational problems with great success. The proposed approach is compared with a wide range of evolutionary feature and instance selection methods for classification. The results contrasted through non-parametric statistical tests show that our model outperforms previously proposed evolutionary approaches for performing data reduction processes in combination with the nearest neighbor rule.

Analyzing convergence performance of evolutionary algorithms: A statistical approach

The analysis of the performance of different approaches is a staple concern in the design of Computational Intelligence experiments. Any proper analysis of evolutionary optimization algorithms should incorporate a full set of benchmark problems and state-of-the-art comparison algorithms. For the sake of rigor, such an analysis may be completed with the use of statistical procedures, supporting the conclusions drawn. In this paper, we point out that these conclusions are usually limited to the final results, whereas intermediate results are seldom considered. We propose a new methodology for comparing evolutionary algorithms’ convergence capabilities, based on the use of Page’s trend test. The methodology is presented with a case of use, incorporating real results from selected techniques of a recent special issue. The possible applications of the method are highlighted, particularly in those cases in which the final results do not enable a clear evaluation of the differences among several evolutionary techniques.

A Survey on Evolutionary Instance Selection and Generation

The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

Integrating Instance Selection, Instance Weighting, and Feature Weighting for Nearest Neighbor Classifiers by Coevolutionary Algorithms

Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier.

A survey of fingerprint classification Part II

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.