scholarly journals Adaptations of Relief for continuous domains of bioinformatics

2019 ◽  
Vol 8 (1) ◽  
pp. 51
Author(s):  
Andrew Yatsko
Keyword(s):  
Feature Selection ◽  
Data Classification ◽  
Dramatic Reduction ◽  
Selection Methods ◽  
Anisotropic Space ◽  
Manhattan Metric ◽  
Continuous Domains

Relief occupies a niche among feature selection methods for data classification. Filters are faster, wrappers are much slower. Relief is feature-set-aware, same as wrappers. However, it is thought being able to deselect only irrelevant, but not redundant features, same as filters. Iterative Reliefs seek to increase the separation margin between classes in the anisotropic space defined by weighted features. Reliefs for continuous domains are much less developed than for categorical domains. The paper discusses a number of adaptations for continuous spaces with Euclidean or Manhattan metric. The ability of Relief to detect redundant features is demonstrated. A dramatic reduction of the feature-set is achieved in a health diagnostics problem.

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.

Feature Selection Methods for Multiphase Reactors Data Classification

2005 ◽  
Vol 44 (4) ◽  
pp. 1073-1084 ◽  
Author(s):  
Laurentiu A. Tarca ◽  
Bernard P. A. Grandjean ◽  
Faïçal Larachi
Keyword(s):  
Feature Selection ◽  
Data Classification ◽  
Selection Methods ◽  
Multiphase Reactors

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

2016 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Fault Prediction ◽  
Filter Method ◽  
Selection Methods ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

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