Selection of the most useful subset of genes for gene expression-based classification

Paul, Topon Kumar; Iba, Hitoshi

doi:10.1109/cec.2004.1331152

Cited by 19 publications

(12 citation statements)

References 9 publications

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“…More specifically, this paper introduces an approach by incorporating feature selection into the model quality estimator. This optimization is driven by a multi-objective Genetic Algorithm (GA), similar to approaches suggested in [24], [25]. This means the proposed approach is mostly a wrapper method.…”

Section: Adaptive Global Surrogate Modelingmentioning

confidence: 99%

Multi-objective variable subset selection using heterogeneous surrogate modeling and sequential design

Herten

Couckuyt

Deschrijver

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Constructing surrogate models of high-dimensional complex black-box systems from simulation-based data requires an appropriate choice of surrogate model type, as well as identification of the most influential input parameters. As including irrelevant input parameters results in a longer surrogate model training process and potentially increases the risk of overfitting, it is important to identify a small set of relevant parameters during the adaptive modeling phase of the surrogate modeling process. A multi-objective optimization step is proposed to identify both the appropriate model type as well as a parameters subset. The obtained model can be used for evaluation intensive applications such as exploration, sensitivity analysis or optimization.

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Section: Adaptive Global Surrogate Modelingmentioning

confidence: 99%

Multi-objective variable subset selection using heterogeneous surrogate modeling and sequential design

Herten

Couckuyt

Deschrijver

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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“…Table 3 shows the results of three other best performing selection algorithms [14,13,11]. We have chosen these references because they use the same or similar experimental protocol to avoid selection bias.…”

Section: Results and Comparisonsmentioning

confidence: 99%

“…These data sets have largely been used for benchmarking feature selection algorithms, for instance in [14,13,11].…”

Section: Data Setsmentioning

confidence: 99%

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SVM-Based Local Search for Gene Selection and Classification of Microarray Data

Hernández

Duval

Hao

Communications in Computer and Information Science

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Abstract. This paper presents a SVM-based local search (SVM-LS) approach to the problem of gene selection and classification of microarray data. The proposed approach is highlighted by the use of a SVM classifier both as an essential part of the evaluation function and as a "provider" of useful information for designing effective LS algorithms. The SVM-LS approach is assessed on a set of three well-known data sets and compared with some best algorithms from the literature.

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“…The literature offers a large number of solution methods for gene selection which are based on genetic algorithms, often combined with other approaches [19,6,18,17,8,16,4,22]. For instance, the so-called wrapper approach uses GAs to search over the space of gene subsets, the fitness of each subset being evaluated by its classification performance obtained by a given classifier.…”

Section: Introductionmentioning

confidence: 99%

A Study of Crossover Operators for Gene Selection of Microarray Data

Hernández

Duval

Hao

Lecture Notes in Computer Science

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Abstract. Classification of microarray data requires the selection of a subset of relevant genes in order to achieve good classification performance. Several genetic algorithms have been devised to perform this search task. In this paper, we carry out a study on the role of crossover operator and in particular investigate the usefulness of a highly specialized crossover operator called GeSeX (GEne SElection crossover) that takes into account gene ranking information provided by a Support Vector Machine classifier. We present experimental evidences about its performance compared with two other conventional crossover operators. Comparisons are also carried out with several recently reported genetic algorithms on four well-known benchmark data sets.

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Selection of the most useful subset of genes for gene expression-based classification

Cited by 19 publications

References 9 publications

Multi-objective variable subset selection using heterogeneous surrogate modeling and sequential design

Multi-objective variable subset selection using heterogeneous surrogate modeling and sequential design

SVM-Based Local Search for Gene Selection and Classification of Microarray Data

A Study of Crossover Operators for Gene Selection of Microarray Data

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