A novel Hybrid Genetic Local Search Algorithm for feature selection and weighting with an application in strategic decision making in innovation management

Eroğlu, Duygu Yılmaz; Kılıç, Kemal

doi:10.1016/j.ins.2017.04.009

Cited by 44 publications

(5 citation statements)

References 32 publications

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“…We categorized feature selection methods used in automated stroke risk detection models into semisupervised, unsupervised, and supervised methods [35,36], as summarized in Table 1. Semisupervised feature selection methods are suitable for datasets with a small number of labeled samples and a large number of unlabeled samples [37]. The key challenge lies in how to use the labeled samples to efficiently process the unlabeled samples.…”

Section: Introductionmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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Background Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. Objective This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. Methods WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. Results This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. Conclusions This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.

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Section: Introductionmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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“…Particularly, sparsity regularization based algorithms are representative embedded methods and have attracted much attention in recent years [11,34,37–41,50]. Recent advances in feature selection not only improve pattern recognition performance but also expand applications including multi-label classification, innovation management, and microarray and omics data analysis [1,6,13,19,24,25,29,31,33,52]. In this study, we focus on the filter methods that measure the relevancy of features to a pattern recognition problem under study based on information theoretical criteria.…”

Section: Introductionmentioning

confidence: 99%

Feature selection by optimizing a lower bound of conditional mutual information

Peng

Fan

2017

Information Sciences

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A unified framework is proposed to select features by optimizing computationally feasible approximations of high-dimensional conditional mutual information (CMI) between features and their associated class label under different assumptions. Under this unified framework, state-of-the-art information theory based feature selection algorithms are rederived, and a new algorithm is proposed to select features by optimizing a lower bound of the CMI with a weaker assumption than those adopted by existing methods. The new feature selection method integrates a plug-in component to distinguish redundant features from irrelevant ones for improving the feature selection robustness. Furthermore, a novel metric is proposed to evaluate feature selection methods based on simulated data. The proposed method has been compared with state-of-the-art feature selection methods based on the new evaluation metric and classification performance of classifiers built upon the selected features. The experiment results have demonstrated that the proposed method could achieve promising performance in a variety of feature selection problems.

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“…Eroglu and Kilic [19] proposed a novel Hybrid Genetic Local Search Algorithm (HGLSA) in combination with the k-nearest neighbor classifier for simultaneous feature subset selection and feature weighting, particularly for medium-sized data sets. Phan et al [20] proposed a framework, named the GA-SVM, to improve the classification ability, in which the GA is hybridized with Support Vector Machine (SVM) for simultaneously parameter optimization and feature weighting.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Three-Stage Filter-Wrapper Framework for miRNA Subset Selection in Cancer Classification

2018

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Micro-Ribonucleic Acids (miRNAs) are small non-coding Ribonucleic Acid (RNA) molecules that play an important role in the cancer growth. There are a lot of miRNAs in the human body and not all of them are responsible for cancer growth. Therefore, there is a need to propose the novel miRNA subset selection algorithms to remove irrelevant and redundant miRNAs and find miRNAs responsible for cancer development. This paper tries to propose a novel three-stage miRNAs subset selection framework for increasing the cancer classification accuracy. In the first stage, multiple filter algorithms are used for ranking the miRNAs according to their relevance with the class label, and then generating a miRNA pool obtained based on the top-ranked miRNAs of each filter algorithm. In the second stage, we first rank the miRNAs of the miRNA pool by multiple filter algorithms and then this ranking is used to weight the probability of selecting each miRNA. In the third stage, Competitive Swarm Optimization (CSO) tries to find an optimal subset from the weighed miRNAs of the miRNA pool, which give us the most information about the cancer patients. It should be noted that the balance between exploration and exploitation in the proposed algorithm is accomplished by a zero-order Fuzzy Inference System (FIS). Experiments on several miRNA cancer datasets indicate that the proposed three-stage framework has a great performance in terms of both the low error rate of the cancer classification and minimizing the number of miRNAs.

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A novel Hybrid Genetic Local Search Algorithm for feature selection and weighting with an application in strategic decision making in innovation management

Cited by 44 publications

References 32 publications

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Feature selection by optimizing a lower bound of conditional mutual information

A Novel Three-Stage Filter-Wrapper Framework for miRNA Subset Selection in Cancer Classification

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