Microarray data classification using neuro-fuzzy classifier with firefly algorithm

Jinthanasatian, Panudech; Auephanwiriyakul, Sansanee; Theera-Umpon, Nipon

doi:10.1109/ssci.2017.8280967

Cited by 18 publications

(3 citation statements)

References 19 publications

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“…Canedo [29] 0.9060 25.0 Jinthanasatian [30] 0.8743 5.0 Wu [31] 0.9044 NAN Wang [14] 0.9040 9.0 Lu [15] 0.9160 4.0 Prostate Proposed 0.9119 4.0…”

Section: Methods Acc Featuresmentioning

confidence: 99%

A Hybrid Feature-Selection Method Based on mRMR and Binary Differential Evolution for Gene Selection

Yu,

Li,

Xie

et al. 2024

Processes

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The selection of critical features from microarray data as biomarkers holds significant importance in disease diagnosis and drug development. It is essential to reduce the number of biomarkers while maintaining their performance to effectively minimize subsequent validation costs. However, the processing of microarray data often encounters the challenge of the “curse of dimensionality”. Existing feature-selection methods face difficulties in effectively reducing feature dimensionality while ensuring classification accuracy, algorithm efficiency, and optimal search space exploration. This paper proposes a hybrid feature-selection algorithm based on an enhanced version of the Max Relevance and Min Redundancy (mRMR) method, coupled with differential evolution. The proposed method improves the quantization functions of mRMR to accommodate the continuous nature of microarray data attributes, utilizing them as the initial step in feature selection. Subsequently, an enhanced differential evolution algorithm is employed to further filter the features. Two adaptive mechanisms are introduced to enhance early search efficiency and late population diversity, thus reducing the number of features and balancing the algorithm’s exploration and exploitation. The results highlight the improved performance and efficiency of the hybrid algorithm in feature selection for microarray data analysis.

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“…Canedo [29] 0.9060 25.0 Jinthanasatian [30] 0.8743 5.0 Wu [31] 0.9044 NAN Wang [14] 0.9040 9.0 Lu [15] 0.9160 4.0 Prostate Proposed 0.9119 4.0…”

Section: Methods Acc Featuresmentioning

confidence: 99%

A Hybrid Feature-Selection Method Based on mRMR and Binary Differential Evolution for Gene Selection

Yu,

Li,

Xie

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…Theera et al [28] designed a neuro-fuzzy based Firefly algorithm for feature selection of microarray data. The method does not have a clear distinction between Filter and Wrapper, but embeds the neuro-fuzzy technique into the merit-seeking process and the evaluation process of the firefly algorithm.…”

Section: Related Wrokmentioning

confidence: 99%

A Novel Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data

Xie

Chi

Wang

et al. 2022

Preprint

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Obtaining diagnostically important features from microarray data can help researchers understand diseases and study drugs. The challenges come from the high dimensionality, small sample size of the data and the high redundancy among features. To solve this problem, we propose a new hybrid feature selection method based on differential evolution algorithm. The proposed method the proposed method adopts a new binary quantization method to adapt to the characteristics of high-dimensional data, and improves the scaling factor and mutation operator to balance the global search performance and convergence speed of the algorithm. The proposed method also proposes the concept of feature subset correlation in the fitness function to ensure the low redundancy of feature subsets. The experimental results show that the proposed method is advanced and the selected features, are with low redundancy and can effectively distinguish between positive and negative samples.

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“…The ANFIS was also trained with PSO, GA, and whale optimization algorithm (WOA) where MFO was found to be more successful than the other algorithms. In another study, Jinthanasatian et al [14] classified seven microarray datasets specifically lung cancer, ovarian cancer, prostate cancer, leukemia (ALL/ AML), colon cancer, diffuse large B-cell lymphoma (DLBCL), and breast cancer using ANFIS with firefly Algorithm (FA). The comparison results showed that the proposed model gave better results than other existing ANFIS-GA and ANFIS-PSO models.…”

Section: Introductionmentioning

confidence: 99%

Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data

Haznedar

Arslan

Kalınlı

2021

Med Biol Eng Comput

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In the medical field, successful classification of microarray gene expression data is of major importance for cancer diagnosis. However, due to the profusion of genes number, the performance of classifying DNA microarray gene expression data using statistical algorithms is often limited. Recently, there has been an important increase in the studies on the utilization of artificial intelligence methods, for the purpose of classifying large-scale data. In this context, a hybrid approach based on the adaptive neuro-fuzzy inference system (ANFIS), the fuzzy c-means clustering (FCM), and the simulated annealing (SA) algorithm is proposed in this study. The proposed method is applied to classify five different cancer datasets (i.e., lung cancer, central nervous system cancer, brain cancer, endometrial cancer, and prostate cancer). The backpropagation algorithm, hybrid algorithm, genetic algorithm, and the other statistical methods such as Bayesian network, support vector machine, and J48 decision tree are used to compare the proposed approach's performance to other algorithms. The results show that the performance of training FCM-based ANFIS using SA algorithm for classifying all the cancer datasets becomes more successful with the average accuracy rate of 96.28% and the results of the other methods are also satisfactory. The proposed method gives more effective results than the others for classifying DNA microarray cancer gene expression data.

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Microarray data classification using neuro-fuzzy classifier with firefly algorithm

Cited by 18 publications

References 19 publications

A Hybrid Feature-Selection Method Based on mRMR and Binary Differential Evolution for Gene Selection

A Hybrid Feature-Selection Method Based on mRMR and Binary Differential Evolution for Gene Selection

A Novel Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data

Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data

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