Fault Detection in Microgrids Using Combined Classification Algorithms and Feature Selection Methods

2019 ◽  
Author(s):  
S. Ranjbar ◽  
S. Jamali
Keyword(s):  
Feature Selection ◽  
Fault Detection ◽  
Classification Algorithms ◽  
Selection Methods

scholarly journals Research on Feature Selection using SVM

2019 ◽  
Vol 8 (4) ◽  
pp. 7252-7256
Keyword(s):  
Feature Selection ◽  
Data Classification ◽  
Classification Algorithm ◽  
Filter Method ◽  
Classification Algorithms ◽  
Data Handling ◽  
Selection Methods ◽  
Fast Growing ◽  
Digital World ◽  
Processing Data

A very fast and efficient classification algorithm is imperative to any application. Nowadays all kinds of applications produce a huge volume of data. Handling these 5’V characteristics data is really very crucial. While processing data, data classification simplifies the mission. Though many classification algorithms are available, they are not up to the mark to meet the fast growing challenges of current digital world. To fill this gap, feature selection is integrated with classifiers, as Feature selection has proved its impact on performance of classifiers. SVM is one of the most frequently used classifier. In this paper, different feature selection methods have been analyzed by studying 21 articles. This survey makes public that SVM based feature selection works better and widely used. Also in feature selection, filter method is widely used.

A survey on text classification and its applications

2020 ◽  
Vol 18 (3) ◽  
pp. 205-216
Author(s):  
Xujuan Zhou ◽  
Raj Gururajan ◽  
Yuefeng Li ◽  
Revathi Venkataraman ◽  
Xiaohui Tao ◽  
...  
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Research Direction ◽  
Information Resources ◽  
Classification Algorithms ◽  
Selection Methods ◽  
Efficient Technology ◽  
Text Data ◽  
Research Fields ◽  
The Web

Text classification (a.k.a text categorisation) is an effective and efficient technology for information organisation and management. With the explosion of information resources on the Web and corporate intranets continues to increase, it has being become more and more important and has attracted wide attention from many different research fields. In the literature, many feature selection methods and classification algorithms have been proposed. It also has important applications in the real world. However, the dramatic increase in the availability of massive text data from various sources is creating a number of issues and challenges for text classification such as scalability issues. The purpose of this report is to give an overview of existing text classification technologies for building more reliable text classification applications, to propose a research direction for addressing the challenging problems in text mining.

scholarly journals Feature Selection Approaches In Antibody Display

2015 ◽  
Vol 2 ◽  
pp. 16
Author(s):  
Inese Polaka
Keyword(s):  
Feature Selection ◽  
Healthy Donor ◽  
Subset Selection ◽  
Statistical Information ◽  
High Dimensionality ◽  
Classification Algorithms ◽  
Selection Methods ◽  
Specific Data ◽  
Selection Algorithms ◽  
Feature Selection Techniques

Molecular diagnostics tools provide specific data that have high dimensionality due to many factors analyzed in one experiment and few records due to high costs of the experiments. This study addresses the problem of dimensionality in melanoma patient antibody display data by applying data mining feature selection techniques. The article describes feature selection ranking and subset selection approaches and analyzes the performance of various methods evaluating selected feature subsets using classification algorithms C4.5, Random Forest, SVM and Naïve Bayes, which have to differentiate between cancer patient data and healthy donor data. The feature selection methods include correlation-based, consistency based and wrapper subset selection algorithms as well as statistical, information evaluation, prediction potential of rules and SVM feature selection evaluation of single features for ranking purposes.

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