Genetic Programming Based Ensemble System for Microarray Data Classification

Liu, Kun Hong; Tong, Muchenxuan; Xie, Shu Tong; Ng, Vincent

doi:10.1155/2015/193406

Cited by 19 publications

(13 citation statements)

References 36 publications

(36 reference statements)

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“…GP is merely a subset of GA, with the key difference being the structures of the individuals. Individuals in GA are string structured, those in GP tree structured [58]. GP is based on the evolution of a particular population.…”

Section: Classificationmentioning

confidence: 99%

A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

AlMazrua

Alshamlan

2022

IEEE Access

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In the diagnosis and treatment of cancer, cancer classification is a vital issue. Gene selection is much needed to solve the high dimensionality issue in microarray data, small sample size, and noisy. The best way to classify cancer is to select those genes that hold the most informative ones, and this process contributes significantly to the classification performance of microarrays. In this survey, we comprehensively studied hybrid selection methods proposed since 2017 that may be used for comparison to several other algorithms proposed for gene selection in cancer classification in the past and looked to see if there are any challenges future authors that need to be discussed.

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Section: Classificationmentioning

confidence: 99%

A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

AlMazrua

Alshamlan

2022

IEEE Access

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“…Much work has been devoted to classification using GP and ABC [18][19][20][21][22][23][24][25]. GP based feature selection age layered population structure as a new algorithm for feature selection with classification was compared with other GP versions in [18].…”

Section: Classificationmentioning

confidence: 99%

“…GP achieved higher success as a classification method by selecting fewer features than other conventional methods. Liu et al designed a new GP based ensemble system to classify different cancer types where the system was used to increase the diversity of each ensemble system [21]. ABC was used data clustering on benchmark problems and was compared conventional classification techniques in [22].…”

Section: Classificationmentioning

confidence: 99%

Feature Selection for Classification with Artificial Bee Colony Programming

Arslan¹,

Öztürk²

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

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Feature selection and classification are the most applied machine learning processes. In the feature selection, it is aimed to find useful properties containing class information by eliminating noisy and unnecessary features in the data sets and facilitating the classifiers. Classification is used to distribute data among the various classes defined on the resulting feature set. In this chapter, artificial bee colony programming (ABCP) is proposed and applied to feature selection for classification problems on four different data sets. The best models are obtained by using the sensitivity fitness function defined according to the total number of classes in the data sets and are compared with the models obtained by genetic programming (GP). The results of the experiments show that the proposed technique is accurate and efficient when compared with GP in terms of critical features selection and classification accuracy on well-known benchmark problems.

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“…al used a multiclass SVM to classify multi-class microarray data with the aid of classification confidence measure [48]. And there are also some evolutionary algorithms based ensemble systems proposed to deal with this problem [32,33]. However, there are no enough explorations in using ECOC to this problem.…”

Section: Lorena and Carvalhomentioning

confidence: 99%

A Hierarchical Ensemble of ECOC for cancer classification based on multi-class microarray data

Liu

Zeng

2016

Information Sciences

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The difficulty of the cancer classification using multi-class microarray datasets lies in that there are only a few samples in each class. To effectively solve such a problem, we propose a hierarchical ensemble strategy, named as Hierarchical Ensemble of Error Correcting Output Codes (HE-ECOC). In this strategy, different feature subsets extracted from a dataset are used as inputs for three data-dependent ECOC algorithms, so as to produce different ECOC coding matrices. The mutual diversity degrees among these coding matrices are then calculated based on two schemes, named as the maximizing local diversity (MLD) and the maximizing global diversity (MGD) schemes. Both schemes can choose diverse coding matrices generated by the same or different ECOC algorithm(s), and the average fusion scheme is used to fuse the outputs of base learners. In the experiments, it is found that both MLD and MGD based HE-ECOC strategies work stably, and outperform individual single ECOC algorithms. In contrast with some ensemble systems, HE-ECOC generates a more robust ensemble system, and achieves better performance in most case. In short, HE-ECOC is a promising solution for the multi-class problem. The matlab code is available upon request.

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Genetic Programming Based Ensemble System for Microarray Data Classification

Cited by 19 publications

References 36 publications

A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

Feature Selection for Classification with Artificial Bee Colony Programming

A Hierarchical Ensemble of ECOC for cancer classification based on multi-class microarray data

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