Binary black hole algorithm for feature selection and classification on biological data

• Published in: Applied soft computing 2017-07, Vol.56, p.94-106
• Main Authors: Pashaei, Elnaz, Aydin, Nizamettin
• Format: Article
• Language: English
• Subjects: Biomedical data; Black hole optimization algorithm; Decision tree algorithms; Feature selection; Particle swarm optimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.03.002

Average solution quality of one filter and four wrapper approaches on 8 medical datasets [Display omitted] •A binary version of the Black Hole Algorithm (BBHA) for solving discrete problems is proposed.•Proposed algorithm was compared to 6 well known decision tree classifiers.•Experimental results demonstrate that Random Forest is the best decision tree algorithm•The proposed BBHA wrapper based feature selection approach outperforms the performances of other algorithms.•The proposed method also performed much faster, needs single parameter for configuring the model, and is simple to understand. Biological data often consist of redundant and irrelevant features. These features can lead to misleading in modeling the algorithms and overfitting problem. Without a feature selection method, it is difficult for the existing models to accurately capture the patterns on data. The aim of feature selection is to choose a small number of relevant or significant features to enhance the performance of the classification. Existing feature selection methods suffer from the problems such as becoming stuck in local optima and being computationally expensive. To solve these problems, an efficient global search technique is needed. Black Hole Algorithm (BHA) is an efficient and new global search technique, inspired by the behavior of black hole, which is being applied to solve several optimization problems. However, the potential of BHA for feature selection has not been investigated yet. This paper proposes a Binary version of Black Hole Algorithm called BBHA for solving feature selection problem in biological data. The BBHA is an extension of existing BHA through appropriate binarization. Moreover, the performances of six well-known decision tree classifiers (Random Forest (RF), Bagging, C5.0, C4.5, Boosted C5.0, and CART) are compared in this study to employ the best one as an evaluator of proposed algorithm. The performance of the proposed algorithm is tested upon eight publicly available biological datasets and is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Correlation based Feature Selection (CFS) in terms of accuracy, sensitivity, specificity, Matthews’ Correlation Coefficient (MCC), and Area Under the receiver operating characteristic (ROC) Curve (AUC). In order to verify the applicability and generality of the BBHA, it was integrated with Naive Bayes (NB) classifier and applied on further datasets on the text