HYEI: A New Hybrid Evolutionary Imperialist Competitive Algorithm for Fuzzy Knowledge Discovery

In recent years, imperialist competitive algorithm (ICA), genetic algorithm (GA), and hybrid fuzzy classification systems have been successfully and effectively employed for classification tasks of data mining. Due to overcoming the gaps related to ineffectiveness of current algorithms for analysing high-dimension independent datasets, a new hybrid approach, named HYEI, is presented to discover generic rule-based systems in this paper. This proposed approach consists of three stages and combines an evolutionary-based fuzzy system with two ICA procedures to generate high-quality fuzzy-classification rules. Initially, the best feature subset is selected by using the embedded ICA feature selection, and then these features are used to generate basic fuzzy-classification rules. Finally, all rules are optimized by using an ICA algorithm to reduce their length or to eliminate some of them. The performance of HYEI has been evaluated by using several benchmark datasets from the UCI machine learning repository. The classification accuracy attained by the proposed algorithm has the highest classification accuracy in 6 out of the 7 dataset problems and is comparative to the classification accuracy of the 5 other test problems, as compared to the best results previously published

HYBRID FLOWER POLLINATION ALGORITHM AND SUPPORT VECTOR MACHINE FOR BREAST CANCER CLASSIFICATION

Microarray technology is a system that enable experts to examine gene profile at molecular level for early disease detection. Machine learning algorithms such as classification are used in detection of dieses from data generated by microarray. It increases the potentials of classification and diagnosis of many diseases such as cancer at gene expression level. Though, numerous difficulties may affect the performance of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data preprocessing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper proposed a new technique for feature selection and classification of breast cancer based on Flower Pollination algorithm (FPA) and Support Vector machine (SVM) using microarray data. The result for this research reveals that FPA-SVM is promising by outperforming the state of the earth Particle Swam Optimization algorithm with 80.11% accuracy. Â

Hybrid feature selection of breast cancer gene expression microarray data based on metaheuristic methods: a comprehensive review

Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. Nevertheless, the microarray datasets suffer from a large number of gene expression levels, limited sample size, and irrelevant features. Additionally, datasets are often asymmetrical, where the number of samples from different classes is not balanced. These limitations make it difficult to determine the actual features that contribute to the existence of cancer classification in the gene expression profiles. Various accurate feature selection methods exist, and they are being widely applied. The objective of feature selection is to search for a relevant, discriminant feature subset from the basic feature space. In this review, we aim to compile and review the latest hybrid feature selection methods based on bio-inspired metaheuristic methods and wrapper methods for the classification of BC and other types of cancer

A Survey of Feature Selection Strategies for DNA Microarray Classification

Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, cross-validation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features. Keywords: Brief survey; DNA microarray data; feature selection; filter methods; wrapper methods; embedded methods; and hybrid methods. DOI: 10.7176/CEIS/14-2-01 Publication date:March 31st 202

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process

EGFAFS:A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data

Filter-Wrapper Methods For Gene Selection In Cancer Classification

In microarray gene expression studies, finding the smallest subset of informative genes from microarray datasets for clinical diagnosis and accurate cancer classification is one of the most difficult challenges in machine learning task. Many researchers have devoted their efforts to address this problem by using a filter method, a wrapper method or a combination of both approaches. A hybrid method is a hybridisation approach between filter and wrapper methods. It benefits from the speed of the filter approach and the accuracy of the wrapper approach. Several hybrid filter-wrapper methods have been proposed to select informative genes. However, hybrid methods encounter a number of limitations, which are associated with filter and wrapper approaches. The gene subset that is produced by filter approaches lacks predictiveness and robustness. The wrapper approach encounters problems of complex interactions among genes and stagnation in local optima. To address these drawbacks, this study investigates filter and wrapper methods to develop effective hybrid methods for gene selection. This study proposes new hybrid filter-wrapper methods based on Maximum Relevancy Minimum Redundancy (MRMR) as a filter approach and adapted bat-inspired algorithm (BA) as a wrapper approach. First, MRMR hybridisation and BA adaptation are investigated to resolve the gene selection problem. The proposed method is called MRMR-BA

Mutable composite firefly algorithm for gene selection in microarray based cancer classification

Cancer classification is critical due to the strenuous effort required in cancer treatment and the rising cancer mortality rate. Recent trends with high throughput technologies have led to discoveries in terms of biomarkers that successfully contributed to cancerrelated issues. A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. However, the existing hybrid approaches with metaheuristic optimization algorithms in feature selection (specifically in gene selection) are not generalized enough to efficiently classify most cancer microarray data while maintaining a small set of genes. This leads to the classification accuracy and genes subset size problem. Hence, this study proposed to modify the Firefly Algorithm (FA) along with the Correlation-based Feature Selection (CFS) filter for the gene selection task. An improved FA was proposed to overcome FA slow convergence by generating mutable size solutions for the firefly population. In addition, a composite position update strategy was designed for the mutable size solutions. The proposed strategy was to balance FA exploration and exploitation in order to address the local optima problem. The proposed hybrid algorithm known as CFS-Mutable Composite Firefly Algorithm (CFS-MCFA) was evaluated on cancer microarray data for biomarker selection along with the deployment of Support Vector Machine (SVM) as the classifier. Evaluation was performed based on two metrics: classification accuracy and size of feature set. The results showed that the CFS-MCFA-SVM algorithm outperforms benchmark methods in terms of classification accuracy and genes subset size. In particular, 100 percent accuracy was achieved on all four datasets and with only a few biomarkers (between one and four). This result indicates that the proposed algorithm is one of the competitive alternatives in feature selection, which later contributes to the analysis of microarray data