scholarly journals Enhancing the Capability of IDS using Fuzzy Rough Classifier with Genetic Search Feature Reduction

2014 ◽  
Vol 2 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Ashalata Panigrahi ◽  
Manas Ranjan Patra
Keyword(s):  
Feature Reduction ◽  
Genetic Search ◽  
Search Feature

scholarly journals A combinatorial cooperative-tabu search feature reduction approach

2013 ◽  
Author(s):  
E. Ansari ◽  
M.H. Sadreddini ◽  
B. Sadeghi Bigham ◽  
F. Alimardani
Keyword(s):  
Tabu Search ◽  
Feature Reduction ◽  
Search Feature ◽  
Reduction Approach

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 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

Locating and Extracting Human Face in a Cluttered Scene by using Genetic Search

1998 ◽  
Vol 118 (5) ◽  
pp. 727-736
Author(s):  
Hertog Nugroho ◽  
Shigeyoshi Takahashi ◽  
Yoshiteru Ooi ◽  
Shinji Ozawa
Keyword(s):  
Human Face ◽  
Genetic Search

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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