scholarly journals Retinal artery/vein classification using genetic-search feature selection

2018 ◽  
Vol 161 ◽  
pp. 197-207 ◽  
Author(s):  
Fan Huang ◽  
Behdad Dashtbozorg ◽  
Tao Tan ◽  
Bart M. ter Haar Romeny
Keyword(s):  
Feature Selection ◽  
Genetic Search ◽  
Search Feature ◽  
Retinal Artery

Comparative Study of Classification Models with Genetic Search Based Feature Selection Technique

2020 ◽  
pp. 773-783
Author(s):  
Sanat Kumar Sahu ◽  
A. K. Shrivas
Keyword(s):  
Feature Selection ◽  
Radial Basis Function Network ◽  
Research Work ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Classification Models ◽  
Feature Selection Technique ◽  
Genetic Search ◽  
Selection Technique ◽  
Search Feature

Feature selection plays a very important role to retrieve the relevant features from datasets and computationally improves the performance of a model. The objective of this study is to evaluate the most important features of a chronic kidney disease (CKD) dataset and diagnose the CKD problem. In this research work, the authors have used a genetic search with the Wrapper Subset Evaluator method for feature selection to increase the overall performance of the classification model. They have also used Bayes Network, Classification and Regression Tree (CART), Radial Basis Function Network (RBFN) and J48 classifier for classification of CKD and non-CKD data. The proposed genetic search based feature selection technique (GSBFST) selects the best features from CKD dataset and compares the performance of classifiers with proposed and existing genetic search feature selection techniques (FSTs). All classification models give the better result with proposed GSBFST as compared to without FST and existing genetic search FSTs.

scholarly journals A feature selection algorithm combining information gain and multi-objective genetic search for intrusion detection system

2021 ◽  
Vol 336 ◽  
pp. 08008
Author(s):  
Tao Xie
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Information Gain ◽  
Detection System ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Genetic Search ◽  
Multi Objective

In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features to find the optimal feature combination. We classified the Kddcup98 dataset into five classes, DOS, PROBE, R2L, and U2R, and conducted numerous experiments on each class. Experimental results show that for each class of attack, the proposed algorithm can not only speed up the feature selection, but also significantly improve the detection rate of the algorithm.

Neighbourhood search feature selection method for content-based mammogram retrieval

2016 ◽  
Vol 55 (3) ◽  
pp. 493-505 ◽  
Author(s):  
D. Abraham Chandy ◽  
A. Hepzibah Christinal ◽  
Alwyn John Theodore ◽  
S. Easter Selvan
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Search Feature ◽  
Neighbourhood Search

scholarly journals Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Jaesung Lee ◽  
Wangduk Seo ◽  
Dae-Won Kim
Keyword(s):  
Feature Selection ◽  
Empirical Studies ◽  
Feature Selection Method ◽  
Budget Constraint ◽  
Feature Subset ◽  
Evolutionary Search ◽  
Selection Methods ◽  
Multilabel Classification ◽  
Genetic Search ◽  
Real World Datasets

Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods.

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