Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

Ahuja, Jyoti; Ratnoo, Saroj

doi:10.18760/ic.14120153

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Ahuja and Ratnoo [31] proposed a hybrid approach for feature selection which employs a Multi-Objective Genetic Algorithm at filter phase based on several criteria and a simple GA at the wrapper phase which optimizes based on SVM classifier.…”

Section: Hybrid Methodsmentioning

confidence: 99%

A Stable SVM-RFE Feature Selection Method for Gene Expression Data

2019

ijeat

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Feature Selection techniques are generally employed to remove the inessential attributes before machine learning technique could be applied. It thus plays an extremely important role by eliminating the unnecessary features that do not contribute and sometimes degrade the performance and prediction accuracy of the machine learning technique. With the growth of dimensionality of data, Feature Selection becomes even more important because it helps to reduce the dimensions of data and hence decreases the requisite memory and computational complexity of the machine learning techniques. Support vector machine-recursive feature elimination (SVM-RFE) has proven to be an efficient wrapper feature selection technique which continues to be widely utilized in many applications, especially in classification of gene expression data. From the perspective of this data, not only the precision in classification but also the stability of Feature Selection method plays an important role. Nonetheless, the topic of stability is ignored in study of feature selection algorithms. To improve the stability of RFE method, a fusion of Information Gain and RFE (IG-RFE-SVM) method is proposed in this paper. Experimental studies show that IG-RFE-SVM outperforms SVM-RFE method in terms of stability

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Section: Hybrid Methodsmentioning

confidence: 99%

A Stable SVM-RFE Feature Selection Method for Gene Expression Data

2019

ijeat

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“…Therefore, Feature selection is considered as multiobjective optimization problem. Multi-objective evolutionary algorithm optimizes multiple objective functions simultaneously (Ahuja & Ratnoo, 2015;Anusha & Sathiaseelan, 2015;Grandchamp et al, 2015;Khan & Baig, 2015;Saroj & Jyoti, 2014;Spolaor et al, 2010;Spolaôr et al, 2017;Xue et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

“…Literature confirms feature selection as a multi-objective problem. Several authors have applied multi-objective metaheuristics for feature subset selection (Ahuja & Ratnoo, 2015;Anusha & Sathiaseelan, 2015;Grandchamp et al, 2015;Khan & Baig, 2015;Saroj & Jyoti, 2014;Spolaôr et al, 2017;Spolaor et al, 2010;Xue et al, 2014). The authors were successful in producing multiple feature subsets instead of a single best subset.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection using PMOGA for Microarray Datasets

Rathee¹,

Ratnoo²,

Ahuja³

2022

JSR

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Microarray technology is significantly impacting the community to know the primary characteristic underlining the expansion and growth of genes. Regardless of its many helpful relevance in analysis of drug detection and disease diagnosis; microarray data has turned into a dispute for various bio-analysts. The dimensionality problem in Microarrays leads to expansion of some new methods. The dilemma of dimensionality reduction in terms of features has been taken as a multi-objective optimization problem, thus, can be solved by using some multi-objective optimization techniques.Yet reduction in features takes a lot of time for final submission, therefore, parallel genetic algorithms will do this task in a more efficient way by parallel optimization of multiple distinct parts of a dataset. In this paper, we have designed a new combined method for parallel implementation of gene selection in multi-objective perspective named as PMOGA. Individual migration strategy is followed to improve the parallel searching speed for improving the efficiency of the proposed algorithm. A comparative study of the proposed PMOGA has been done on eight most referenced datasets. The obtained results confirm the supremacy of MOGA based parallel approach over the other approaches based on different performance measures.

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“…where Fitness2 (X) is the average correlation between the selected feature subset B (consists of all those features of X where x i = 1) and the corresponding class attribute y [20]. Update the repository A 14: end for 15: Use CD measure to obtain best out of bests from the repository.…”

mentioning

confidence: 99%

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

et al. 2022

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The rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community's overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and humanitarian initiatives. Almost 10.1% infants died in the United States of America (USA) in 2021. Therefore, this paper aims to extract and understand the various influential factors causing infant deaths in the USA. A crowding distance-based multi-objective ant lion optimization (MOALO-CD) is proposed here with statistical evidence for feature selection. The proposed technique is compared with competitive metaheuristic models such as multi-objective genetic algorithm based on crowding distance (MOGA-CD), multi-objective filter approaches, and recursive feature elimination. Various machine learning classifiers are applied to the selected feature subset obtained from MOALO-CD on the USA's infant dataset. Extensive experimental results indicate that the proposed model outperforms the existing metaheuristic approaches in terms of Generational Distance, Inverted Generational Distance, Spread, and Hyper volume. Also, the comparative analysis of various machine learning models reveals that random forest achieves significantly better performance on the feature subset obtained from MOALO-CD.

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Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

Cited by 6 publications

References 11 publications

A Stable SVM-RFE Feature Selection Method for Gene Expression Data

A Stable SVM-RFE Feature Selection Method for Gene Expression Data

Feature Selection using PMOGA for Microarray Datasets

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

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