The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature. The conceptual advantages of the multiobjective formulation are discussed and an evolutionary approach to the problem is developed. The resulting algorithm, multiobjective clustering with automatic k-determination, is compared with a number of well-established single-objective clustering algorithms, a modern ensemble technique, and two methods of model selection. The experiments demonstrate that the conceptual advantages of multiobjective clustering translate into practical and scalable performance benefits

An Evolutionary Approach to Multiobjective Clustering

The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionary algorithms have been found to be particularly useful for automatic processing of large quantities of raw noisy data for optimal parameter setting and to discover significant and meaningful information. Many real-life data mining problems involve multiple conflicting measures of performance, or objectives, which need to be optimized simultaneously. Under this context, multiobjective evolutionary algorithms are gradually finding more and more applications in the domain of data mining since the beginning of the last decade. In this two-part paper, we have made a comprehensive survey on the recent developments of multiobjective evolutionary algorithms for data mining problems. In this paper, Part I, some basic concepts related to multiobjective optimization and data mining are provided. Subsequently, various multiobjective evolutionary approaches for two major data mining tasks, namely feature selection and classification, are surveyed. In Part II of this paper, we have surveyed different multiobjective evolutionary algorithms for clustering, association rule mining, and several other data mining tasks, and provided a general discussion on the scopes for future research in this domain.

A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I

https://sci2s.ugr.es/keel/pdf/specific/articulo/38-2-E.pdf

Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

Ensembles for feature selection: A review and future trends

This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts,” giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique.

/pdf/multiobjective-optimization-in-bioinformatics-and-1b9bfetx3h.pdf

Multiobjective Optimization in Bioinformatics and Computational Biology

In this paper a methodology for feature selection in unsupervisedlearning is proposed. It makes use of a multi-objectivegenetic algorithm where the minimization of thenumber of features and a validity index that measures thequality of clusters have been used to guide the search towardsthe more discriminant features and the best numberof clusters. The proposed strategy is evaluated usingtwo synthetic data sets and then it is applied to handwrittenmonth word recognition. Comprehensive experimentsdemonstrate the feasibility and efficiency of the proposedmethodology.

/pdf/unsupervised-feature-selection-using-multi-objective-genetic-24rm7kz5pj.pdf

Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition

Summary. Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The underpinning paradigm is the “overproduce and choose”. The algorithm operates in two levels. Firstly, it performs feature selection in order to generate a set of classifiers and then it chooses the best team of classifiers. In order to show its robustness, the method is evaluated in two different contexts: supervised and unsupervised feature selection. In the former, we have considered the problem of handwritten digit recognition and used three different feature sets and multi-layer perceptron neural networks as classifiers. In the latter, we took into account the problem of handwritten month word recognition and used three different feature sets and hidden Markov models as classifiers. Experiments and comparisons with classical methods, such as Bagging and Boosting, demonstrated that the proposed methodology brings compelling improvements when classifiers have to work with very low error rates.

/pdf/feature-selection-for-ensembles-using-the-multi-objective-4360ide2ho.pdf

Feature Selection for Ensembles Using the Multi-Objective Optimization Approach

Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The underpinning paradigm is the "overproduce and choose". The algorithm operates in two levels. Firstly, it performs feature selection in order to generate a set of classifiers and then it chooses the best team of classifiers. In order to show its robustness, the method is evaluated in two different contexts:supervised and unsupervised feature selection. In the former, we have considered the problem of handwritten digit recognition and used three different feature sets and multi-layer perceptron neural networks as classifiers. In the latter, we took into account the problem of handwritten month word recognition and used three different feature sets and hidden Markov models as classifiers. Experiments and comparisons with classical methods, such as Bagging and Boosting, demonstrated that the proposed methodology brings compelling improvements when classifiers have to work with very low error rates. Comparisons have been done by considering the recognition rates only.

/pdf/feature-selection-for-ensembles-applied-to-handwriting-1qjnbgu254.pdf

Feature selection for ensembles applied to handwriting recognition

Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The algorithm operates in two levels. Firstly, it performs feature selection in order to generate a set of classifiers and then it chooses the best team of classifiers. In order to show its robustness, the method is evaluated in two different contexts: supervised and unsupervised feature selection. In the former, we have considered the problem of handwritten digit recognition while in the latter, we took into account the problem of handwritten month word recognition. Experiments and comparisons with classical methods, such as Bagging and Boosting, demonstrated that the proposed methodology brings compelling improvements when classifiers have to work with very low error rates.

/pdf/multi-objective-genetic-algorithms-to-create-ensemble-of-26bepfdnuj.pdf

M. E. Morita

Papers

Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition

Feature Selection for Ensembles Using the Multi-Objective Optimization Approach

Feature selection for ensembles applied to handwriting recognition

Multi-objective genetic algorithms to create ensemble of classifiers

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