Recursive Memetic Algorithm for gene selection in microarray data

Ghosh, Manosij; Begum, Shameem Ara; Sarkar, Ram; Chakraborty, Debasis; Maulik, Ujjwal

doi:10.1016/j.eswa.2018.06.057

Cited by 111 publications

(46 citation statements)

References 55 publications

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“…In Ref. 22, authors have developed a Recursive Memetic Algorithm (RMA) model for gene selection. It was a variant of Memetic Algorithm (MA) and performed much better than MA as well as GA. RMA has been applied on seven microarray datasets, namely, AMLGSE2191, Colon, Di®use large B-cell lymphoma (DLBCL), Leukaemia, Prostate, MLL and SRBCT.…”

Section: Related Workmentioning

confidence: 99%

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

Shukla

Singh

Vardhan

2019

Int. J. Comp. Intel. Appl.

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The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

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Section: Related Workmentioning

confidence: 99%

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

Shukla

Singh

Vardhan

2019

Int. J. Comp. Intel. Appl.

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“…It must be said that the literature already presents classification methods using metaheuristic for numerical optimisation but these differ from ODP. As an example, a genetic algorithm is used in Reference [37] for finding optimal coefficients of a wavelet kernel extreme learning classifier, while a memetic algorithm is used in Reference [38] to optimise a model for gene selection in microarray data. Moreover, past studies made use of a standard PSO in Reference [39] as well as the Artificial Bee Colony paradigm (ABC) [40] for data classification and clustering, respectively.…”

Section: Background and Related Workmentioning

confidence: 99%

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

2019

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This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions.

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“…In addition, k ‐means algorithm is applied for the distribution of individual in the search space and makes a cluster in each iteration. After that, an effective GS have proposed by Ghosh et al called recursive memetic technique which performed much better compared to memetic and GA. Similarly, Dong et al examined granularity level affects both classification accuracy and number of feature subsets for GS during the experiment.…”

Section: Literature Workmentioning

confidence: 99%

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Shukla¹

2019

Computational Intelligence

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Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of a limited number of tissues compared to large number of genes in genomic data, various existing methods have failed to identify a small subset of discriminative genes. To overcome this limitation, in this paper, we developed a new hybrid technique for gene selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook the irrelevant genes and classify cancer accurately. The proposed hybrid gene selection algorithm comprises of two phase. In the first phase, an ensemble gene selection (EGS) method used to filter the noisy and redundant genes in high‐dimensional datasets by combining multilayer and F‐score approaches. Then, an adaptive genetic algorithm based on multipopulation strategy with support vector machine and naïve Bayes (NB) classifiers as a fitness function is applied for gene selection to select the extremely sensible genes from the reduced datasets. The performance of the proposed method is estimated on 10 microarray datasets of numerous tumor. The comprehensive results and various comparisons disclose that EGS has a remarkable impact on the efficacy of the adaptive genetic algorithm with multipopulation strategy and enhance the capability of the proposed approach in terms of convergence rate and solution quality. The experiments results demonstrate the superiority of the proposed method when compared to other standard wrappers regarding classification accuracy and optimal number of genes.

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Recursive Memetic Algorithm for gene selection in microarray data

Cited by 111 publications

References 55 publications

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

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