A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator

Emmanouilidis, Christos; Hunter, Andrew; MacIntyre, John

doi:10.1109/cec.2000.870311

Cited by 90 publications

(48 citation statements)

References 17 publications

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“…With respect to supervised classifiers, in [9,11], multi-objective feature selection procedures that take into account the number of features and the performance of the classifier are proposed. Papers [10,12,13] are focused to unsupervised classification.…”

Section: Unsupervised Multi-objective Feature Selectionmentioning

confidence: 99%

Leveraging cooperation for parallel multi‐objective feature selection in high‐dimensional EEG data

Kimovski

Ortega

Ortíz

et al. 2015

Concurrency and Computation

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SUMMARYBioinformatics applications frequently involve high-dimensional model building or classification problems that require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. Thus, feature selection has become an important issue on these applications. Moreover, several approaches for supervised and unsupervised feature selections as a multi-objective optimization problem have been recently proposed to cope with issues on performance evaluation of classifiers and models. As parallel processing constitutes an important tool to reach efficient approaches that make it possible to tackle complex problems within reasonable computing times, in this paper, alternatives for the cooperation of subpopulations in multi-objective evolutionary algorithms have been identified and classified, and several procedures have been implemented and evaluated on some synthetic and Brain-Computer Interface datasets. The results show different improvements achieved in the solution quality and speedups, depending on the cooperation alternative and dataset. We show alternatives that even provide superlinear speedups with only small reductions in the solution quality, besides another cooperation alternative that improves the quality of the solutions with speedups similar to, or only slightly higher than, the speedup obtained by the parallel fitness evaluation in a master-worker implementation (the alternative used as reference that behaves as the corresponding sequential multi-objective approach).

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Section: Unsupervised Multi-objective Feature Selectionmentioning

confidence: 99%

Leveraging cooperation for parallel multi‐objective feature selection in high‐dimensional EEG data

Kimovski

Ortega

Ortíz

et al. 2015

Concurrency and Computation

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“…We use a Subset Size-Oriented Common Feature Crossover Operator (SSOCF) [2] which keeps useful informative blocks and produces offspring's which have the same distribution than the parents. Offsprings are kept, only if they fit better than the least good individual of the population.…”

Section: Crossovermentioning

confidence: 99%

Feature Selection in Data-Mining for Genetics Using Genetic Algorithm

Rajavarman¹,

Rajagopalan²

2007

J. of Computer Science

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Abstract:We discovered genetic features and environmental factors which were involved in multifactorial diseases. To exploit the massive data obtained from the experiments conducted at the General Hospital, Chennai, data mining tools were required and we proposed a 2-Phase approach using a specific genetic algorithm. This heuristic approach had been chosen as the number of features to consider was large (upto 3654 for biological data under our study). Collected data indicated for pairs of affected individuals of a same family their similarity at given points (locus) of their chromosomes. This was represented in a matrix where each locus was represented by a column and each pairs of individuals considered by a row. The objective was first to isolate the most relevant associations of features and then to class individuals that had the considered disease according to these associations. For the first phase, the feature selection problem, we used a genetic algorithm (GA). To deal with this very specific problem, some advanced mechanisms had been introduced in the genetic algorithm such as sharing, random immigrant, dedicated genetic operators and a particular distance operator had been defined. Then, the second phase, a clustering based on the features selected during the previous phase, will use the clustering algorithm k-means.

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“…In this case, the authors were dealing with a handwritten word recognition task in which the original NSGA [11] was used as their search engine. It is also possible to introduce additional objectives related, for example, to cost [34] or some problem-specific characteristics [35]. This illustrates the flexibility that MOEAs provide when applied to pattern recognition tasks.…”

Section: Some Applications In Pattern Recognitionmentioning

confidence: 99%

Evolutionary Multi-Objective Optimization: Basic Concepts and Some Applications in Pattern Recognition

Coello

2011

Lecture Notes in Computer Science

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Abstract. This paper provides a brief introduction to the so-called multi-objective evolutionary algorithms, which are bio-inspired metaheuristics designed to deal with problems having two or more (normally conflicting) objectives. First, we provide some basic concepts related to multi-objective optimization and a brief review of approaches available in the specialized literature. Then, we provide a short review of applications of multi-objective evolutionary algorithms in pattern recognition. In the final part of the paper, we provide some possible paths for future research in this area, which are promising, from the author's perspective.

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A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator

Cited by 90 publications

References 17 publications

Leveraging cooperation for parallel multi‐objective feature selection in high‐dimensional EEG data

Leveraging cooperation for parallel multi‐objective feature selection in high‐dimensional EEG data

Feature Selection in Data-Mining for Genetics Using Genetic Algorithm

Evolutionary Multi-Objective Optimization: Basic Concepts and Some Applications in Pattern Recognition

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