A Comparative Study of Cancer Classification Methods Using Microarray Gene Expression Profile

Alshamlan, Hala; Badr, Ghada; Al-Ohali, Yousef

doi:10.1007/978-981-4585-18-7_44

Cited by 22 publications

(15 citation statements)

References 31 publications

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“…Generally, the better SVM classifier seeks to balance between increasing the margin and reducing the number of errors. In our recent comparative study [ 14 ], we showed that machine learning classification methods produce accurate result with minimum number of genes. There are many machine learning techniques that have been applied for classifying microarray dataset, including SVM, K nearest neighbor (KNN), random forest (RF), artificial neural network (ANN), and naive Bayes (NB).…”

Section: Methodsmentioning

confidence: 99%

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mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

Alshamlan

Badr

Al-Ohali

2015

BioMed Research International

Self Cite

184

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An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

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Section: Methodsmentioning

confidence: 99%

“…In this paper, we measure the efficiency of gene selection techniques using a support vector machine (SVM) as a classifier. An SVM displayed substantial benefits when compared to other classification approaches [ 14 ]. It is difficult to find a linear classifier to separate different classes in the dataset.…”

Section: Introductionmentioning

confidence: 99%

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

Alshamlan

Badr

Al-Ohali

2015

BioMed Research International

Self Cite

184

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“…Support vector machine performs very well on most of the problems in high dimensional space. It is difficult to find a linear classifier to separate different classes [18]. Naïve Bayes is quite sensitive to the presence of redundant and irrelevant predicted attributes [19].…”

Section: Experimental Setup and Datasetmentioning

confidence: 99%

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

Nagpal

Gaur

2017

CIT

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Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP) for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure) uses a firefly optimization algorithm in the local search optimization phase of the greedy randomized adaptive search procedure (GRASP). Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios) which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FF-GRASP performs better on seven out of nine datasets.

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“…In this paper, we measure the efficiency of gene selection techniques using a support vector machine (SVM) as a classifier. An SVM displayed substantial benefits when compared to other classification approaches [9]. It is challenging to construct a linear classifier to separate the classes of data.…”

Section: Introductionmentioning

confidence: 99%

ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification

Alshamlan¹,

Badr²,

Al-Ohali³

2016

IJMLC

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In this paper, we propose apply ABC algorithm in analyzing microarray dataset. In addition, we propose an innovative hybrid classification model, Support Vector Machine (SVM) with ABC algorithm, to measure the classification accuracy for selected genes. We evaluate the performance of the proposed ABC-SVM algorithm by conducting extensive experiments on six binary and multi-class microarrays dataset. Furthermore, we compare our proposed ABC-SVM algorithm with previously known techniques. The experimental results prove that ABC-SVM algorithm is promising approach for solving gene selection and cancer classification problems, and achieves the highest classification accuracy together with the lowest average of selected genes compared to previously suggested methods.

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A Comparative Study of Cancer Classification Methods Using Microarray Gene Expression Profile

Cited by 22 publications

References 31 publications

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification

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