The minimum feature subset selection problem

1997 ◽  
Vol 12 (2) ◽  
pp. 145-153 ◽  
Author(s):  
Bin Chen ◽  
Jiarong Hong ◽  
Yadong Wang
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Selection Problem ◽  
Feature Subset

Solving feature subset selection problem by a Parallel Scatter Search

2006 ◽  
Vol 169 (2) ◽  
pp. 477-489 ◽  
Author(s):  
Félix Garcı́a López ◽  
Miguel Garcı́a Torres ◽  
Belén Melián Batista ◽  
José A. Moreno Pérez ◽  
J. Marcos Moreno-Vega
Keyword(s):  
Scatter Search ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Selection Problem ◽  
Feature Subset

scholarly journals Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 76 ◽  
Author(s):  
Mehreen Naz ◽  
Kashif Zafar ◽  
Ayesha Khan
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Selection Problem ◽  
Support Vector ◽  
Feature Subset ◽  
Hybrid Technique ◽  
Computational Performance

Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists of a hybrid technique of minimum redundancy and maximum relevance (mRMR) and Forest Optimization Algorithm (FOA)-based feature selection. Ensemble-based classification is implemented to optimize the results of individual classifiers. The Forest Optimization Algorithm as a feature selection technique has been applied to various classification datasets from the UCI machine learning repository. The classifiers used for ensemble methods for UCI repository datasets are the k-Nearest Neighbor (k-NN) and Naïve Bayes (NB). For the classification of sentiments, 15–20% improvement has been recorded. The dataset used for classification of sentiments is Blitzer’s dataset consisting of reviews of electronic products. The results are further improved by ensemble of k-NN, NB, and Support Vector Machine (SVM) with an accuracy of 95% for the classification of sentiment tasks.

Novel multiobjective TLBO algorithms for the feature subset selection problem

2018 ◽  
Vol 306 ◽  
pp. 94-107 ◽  
Author(s):  
Hakan Ezgi Kiziloz ◽  
Ayça Deniz ◽  
Tansel Dokeroglu ◽  
Ahmet Cosar
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Selection Problem ◽  
Feature Subset
Sign in / Sign up

Export Citation Format

Share Document