scholarly journals Feature Selection Algorithm using Fuzzy Rough Sets for Predicting Cervical Cancer Risks

2010 ◽  
Vol 4 (8) ◽  
Author(s):  
Vandar Kuzhali Jagannathan ◽  
Rajendran Govind ◽  
Srinivasan V ◽  
Siva Kumar Ganapathi
Keyword(s):  
Cervical Cancer ◽  
Feature Selection ◽  
Rough Sets ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Cancer Risks ◽  
Fuzzy Rough Sets

scholarly journals A Feature Selection Approach in the Study of Azorean Proverbs

2014 ◽  
pp. 38-58 ◽  
Author(s):  
Luís Cavique ◽  
Armando B. Mendes ◽  
Matthias Funk ◽  
Jorge M. A. Santos
Keyword(s):  
Feature Selection ◽  
Real World ◽  
Rough Sets ◽  
Noisy Data ◽  
Logical Analysis ◽  
Logical Analysis Of Data ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Selection Approach ◽  
Feature Selection Approach

A paremiologic (study of proverbs) case is presented as part of a wider project based on data collected among the Azorean population. Given the considerable distance between the Azores islands, the authors present the hypothesis that there are significant differences in the proverbs from each island, thus permitting the identification of the native island of the interviewee based on his or her knowledge of proverbs. In this chapter, a feature selection algorithm that combines Rough Sets and the Logical Analysis of Data (LAD) is presented. The algorithm named LAID (Logical Analysis of Inconsistent Data) deals with noisy data, and the authors believe that an important link was established between the two different schools with similar approaches. The algorithm was applied to a real world dataset based on data collected using thousands of interviews of Azoreans, involving an initial set of twenty-two thousand Portuguese proverbs.

scholarly journals A Mixed Feature Selection Method Considering Interaction

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zilin Zeng ◽  
Hongjun Zhang ◽  
Rui Zhang ◽  
Youliang Zhang
Keyword(s):  
Feature Selection ◽  
Rough Sets ◽  
Feature Selection Method ◽  
Feature Space ◽  
Feature Interaction ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Neighborhood Rough Sets ◽  
Real World Datasets ◽  
Neighborhood Interaction

Feature interaction has gained considerable attention recently. However, many feature selection methods considering interaction are only designed for categorical features. This paper proposes a mixed feature selection algorithm based on neighborhood rough sets that can be used to search for interacting features. In this paper, feature relevance, feature redundancy, and feature interaction are defined in the framework of neighborhood rough sets, the neighborhood interaction weight factor reflecting whether a feature is redundant or interactive is proposed, and a neighborhood interaction weight based feature selection algorithm (NIWFS) is brought forward. To evaluate the performance of the proposed algorithm, we compare NIWFS with other three feature selection algorithms, including INTERACT, NRS, and NMI, in terms of the classification accuracies and the number of selected features with C4.5 and IB1. The results from ten real world datasets indicate that NIWFS not only deals with mixed datasets directly, but also reduces the dimensionality of feature space with the highest average accuracies.

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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