A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms

Naseriparsa, Mehdi; Bidgoli, Amir-Masoud; Varaee, Touraj

doi:10.5120/12065-8172

Cited by 33 publications

(17 citation statements)

References 17 publications

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“…GA wrapper is the most popular method applied in the area of feature selection, and it has shown its efficacy in various areas (medical diagnosis [8], computer vision/image processing [9], text mining [10], bioinformatics [11], industrial applications [12].…”

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confidence: 99%

Information gain directed genetic algorithm wrapper feature selection for credit rating

Jadhav

Jenkins

2018

Applied Soft Computing

249

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Financial credit scoring is one of the most crucial processes in the finance industry sector to be able to assess the credit-worthiness of individuals and enterprises. Various statistics-based machine learning techniques have been employed for this task. "Curse of Dimensionality" is still a significant challenge in machine learning techniques. Some research has been carried out on Feature Selection (FS) using genetic algorithm as wrapper to improve the performance of credit scoring models. However, the challenge lies in finding an overall best method in credit scoring problems and improving the time-consuming process of feature selection. In this study, the credit scoring problem is investigated through feature selection to improve classification performance. This work proposes a novel approach to feature selection in credit scoring applications, called as Information Gain Directed Feature Selection algorithm (IGDFS), which performs the ranking of features based on information gain, propagates the top m features through the GA wrapper (GAW) algorithm using

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confidence: 99%

Information gain directed genetic algorithm wrapper feature selection for credit rating

Jadhav

Jenkins

2018

Applied Soft Computing

249

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“…In previous research efforts (Dash & Liu, 2003;Naseriparsa et al, 2013) the performance evaluation of the Feature Selection Algorithm is based on a) the Average Number of Misclassified Samples (AMS, see equation 1) and b) on the Average Relative Absolute Error (ARAE, see equation 2).…”

Section: Feature Selectionmentioning

confidence: 99%

Balancing Soil Parameters and Farmers Budget by Feature Selection and Ordered Weighted Averaging

Mokarram¹,

Shaygan²,

Miliaresis³

2018

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A method is presented allowing farmers at Shiraz in the Fars province of Iran to balance in between their budget and the soil parameters. First, the three alternatives (Best-First, Greedy-Stepwise and Ranker) of the Feature Selection Method identify the most critical soil fertility parameters. Training data model evaluation indicate that the Greedy-Stepwise feature selection algorithm (with attribute evaluator of CFS-Subset-Eval) presents the highest accuracy for the particular study area. Soil fertility is found to highly depends on Potassium, Phosphor, and Organic Carbon while Copper, Iron, Manganese, and Zinc dependencies are rejected. Finally, by utilizing Ordered Weighted Averaging, six maps with different risk levels in terms of the soil fertility are constructed allowing alternative management options according to the farmers budget. The major scientific contributions are summarized to a) the identification of soil fertility parameters, and the b) construction of maps modeling soil fertility for various degrees of uncertainty allowing agricultural cost effective planning in the study area.

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“…For feature subset generation from the relevant features, the searching strategies such as sequential backward floating search (SBFS), extended sequential forward search (ESFS), and sequential forward floating search (SFFS) are also employed [74]. Naseriparsa et al proposed a hybrid method using information gain and genetic algorithm-based searching method combined with a supervised learning algorithm [75]. Huda et al developed a hybrid feature selection method by combining the mutual information (MI) and artificial neural network (ANN) [76].…”

Section: Hybrid Methodsmentioning

confidence: 99%

Literature Review on Feature Selection Methods for High-Dimensional Data

Singh¹,

Balamurugan²,

Leavline³

2016

IJCA

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Feature selection plays a significant role in improving the performance of the machine learning algorithms in terms of reducing the time to build the learning model and increasing the accuracy in the learning process. Therefore, the researchers pay more attention on the feature selection to enhance the performance of the machine learning algorithms. Identifying the suitable feature selection method is very essential for a given machine learning task with highdimensional data. Hence, it is required to conduct the study on the various feature selection methods for the research community especially dedicated to develop the suitable feature selection method for enhancing the performance of the machine learning tasks on high-dimensional data. In order to fulfill this objective, this paper devotes the complete literature review on the various feature selection methods for highdimensional data. General TermsLiterature review on feature selection methods, study on feature selection, wrapper-based feature selection, embeddedbased feature selection, hybrid feature selection, filter-based feature selection, feature subset-based feature selection, feature ranking-based feature selection, attribute selection, dimensionality reduction, variable selection, survey on feature selection, feature selection for high-dimensional data, introduction to variable and feature selection, feature selection for classification. KeywordsIntroduction to variable and feature selection, information gain-based feature selection, gain ratio-based feature selection, symmetric uncertainty-based feature selection, subset-based feature selection, ranking-based feature selection, wrapper-based feature selection, embedded-based feature selection, filter-based feature selection, hybrid feature selection, selecting feature from high-dimensional data.

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A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms

Cited by 33 publications

References 17 publications

Information gain directed genetic algorithm wrapper feature selection for credit rating

Information gain directed genetic algorithm wrapper feature selection for credit rating

Balancing Soil Parameters and Farmers Budget by Feature Selection and Ordered Weighted Averaging

Literature Review on Feature Selection Methods for High-Dimensional Data

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