A new local search based hybrid genetic algorithm for feature selection

Kabir, Md. Monirul; Shahjahan, Md.; Murase, Kazuyuki

doi:10.1016/j.neucom.2011.03.034

Cited by 239 publications

(110 citation statements)

References 44 publications

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“…In embedded method[6] [26][34], a feature selection method is incorporated into a learning algorithm and optimized for it. It is also called the hybrid model which is combination of filter and wrapper method.…”

Section: Embedded Methodsmentioning

confidence: 99%

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A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification

2016

IJSR

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Abstract:The rapid growth of World Wide Web has led to explosive growth of information. As most of information is stored in the form of texts, text mining has gained paramount importance. With the high availability of information from diverse sources, the task of automatic categorization of documents has become a vital method for managing, organizing vast amount of information and knowledge discovery. Text classification is the task of assigning predefined categories to documents. The major challenge of text classification is accuracy of classifier and high dimensionality of feature space. These problems can be overcome using Feature Selection. Feature selection is a process of identifying a subset of the most useful features from the original entire set of features. Feature selection (FS) is a strategy that aims at making text document classifiers more efficient and accurate. Feature selection methods provide us a way of reducing computation time, improving prediction performance, and a better understanding of the data. This paper surveys of text classification, several approaches of text classification, feature selection methods and applications of text classifications.

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Section: Embedded Methodsmentioning

confidence: 99%

“…Apart from the sequential search-based FS algorithms, global search-based algorithms (i.e., meta-heuristic algorithms) start searching in a full space rather than partial space for finding high-quality solution [26]. ACO algorithm is used for feature [27], M. Aghdam et al [28], L. Ke et al [29].…”

Section: Related Workmentioning

confidence: 99%

A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification

2016

IJSR

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“…The success of the feature selection process mainly depends on considering two aspects: search strategy and criteria [9]. Different feature selection approaches use different methods to generate subsets and progress the search processes.…”

Section: A Genetic Algorithms For Feature Selectionmentioning

confidence: 99%

Breast Cancer Diagnosis Using a Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

Alzubaidi

Cosma

Brown

et al. 2016

2016 International Conference on Interactive Technologies and Games (ITAG)

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Abstract-Feature Selection is the process of selecting a subset of relevant features (i.e. predictors) for use in the construction of predictive models. This paper proposes a hybrid feature selection approach to breast cancer diagnosis which combines a Genetic Algorithm (GA) with Mutual Information (MI) for selecting the best combination of cancer predictors, with maximal discriminative capability. The selected features are then input into a classifier to predict whether a patient has breast cancer. Using a publicly available breast cancer dataset, experiments were performed to evaluate the performance of the Genetic Algorithm based on the Mutual Information approach with two different machine learning classifiers, namely the k-Nearest Neighbor (K-NN), and Support vector machine (SVM), each tuned using different distance measures and kernel functions, respectively. The results revealed that the proposed hybrid approach is highly accurate for predicting breast cancer, and it is very promising for predicting other cancers using clinical data.

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“…The wrapper approach uses a given learning algorithm to evaluate the feature subsets while the filter approach utilizes the inherent characteristics of the dataset to evaluate the feature subsets, such as the correlation, redundancy, and statistical dependence 6,7 . The wrapper approach usually gets better classification performance since the feature subsets are directly chosen according to their classification accuracies, but it also needs considerable computational time due to the learning algorithm in the evaluation process 8,9 .…”

Section: Introductionmentioning

confidence: 99%

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

Qiu¹

2018

IJCIS

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Feature selection (FS) is a crucial data pre-processing process in classification problems. It aims to reduce the dimensionality of the problem by eliminating irrelevant or redundant features while achieve similar or even higher classification accuracy than using all the features. As a variant of particle swarm optimization (PSO), Bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimizer. However, it also suffers from premature convergence like other PSO algorithms, especially in high-dimensional optimization problems. In order to improve its performance in FS problems, this paper proposes a novel BBPSO based FS method called BBPSO-ACJ. An adaptive chaotic jump strategy is designed to help the stagnated particles make a large change in their searching trajectory. It can enrich the search behavior of BBPSO and prevent the particles from being trapped into local attractors. A new global best updating mechanism is employed to reduce the size of obtained feature subset. The proposed BBPSO-ACJ is compared with eight evolutionary computation (EC) based wrapper methods and two filter methods on nine benchmark datasets with different number of dimensions and instances. The experimenta l results indicate that the proposed method can select the most discriminative features from the entire feature set and achieve significantly better classification performance than other comparative methods.

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A new local search based hybrid genetic algorithm for feature selection

Cited by 239 publications

References 44 publications

A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification

A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification

Breast Cancer Diagnosis Using a Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

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