Towards Benchmarking Feature Subset Selection Methods for Software Fault Prediction

2016 ◽  
pp. 33-58 ◽  
Author(s):  
Wasif Afzal ◽  
Richard Torkar
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Fault Prediction ◽  
Feature Subset ◽  
Selection Methods ◽  
Software Fault Prediction ◽  
Software Fault

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.

scholarly journals Emotional Classification of Acoustic Information With Optimal Feature Subset Selection Methods

2018 ◽  
Vol 7 (2.32) ◽  
pp. 39
Author(s):  
Dr Swarna Kuchibhotla ◽  
Mr Niranjan M.S.R
Keyword(s):  
High Efficiency ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Computational Time ◽  
Feature Subset ◽  
Selection Methods ◽  
Forward Selection ◽  
Optimal Feature Subset ◽  
Optimal Feature

This paper mainly focuses on classification of various Acoustic emotional corpora with frequency domain features using feature subset selection methods. The emotional speech samples are classified into neutral,  happy, fear , anger,  disgust and sad  states by using properties of statistics  of spectral features estimated from Berlin and Spanish emotional utterances. The Sequential Forward Selection(SFS) and Sequential Floating Forward Selection(SFFS)feature subset selection algorithms are  for extracting more informative features. The number of speech emotional samples available for training is smaller than that of the number of features extracted from the speech sample in both Berlin and Spanish corpora which is called curse of dimensionality. Because of this  feature vector of high dimensionality the efficiency of the classifier decreases and at the same time the computational time also increases. For additional  improvement in the efficiency of the classifier  a subset of  features which are optimal is needed and is obtained by using feature subset selection methods. This will enhances the performance of the system with high efficiency and lower computation time. The classifier used in this work is the standard K Nearest Neighbour (KNN) Classifier. Experimental evaluation   proved  that the performance of the classifier is enhanced with SFFS because it vanishes the nesting effect suffered by SFS. The results also showed that an optimal feature subset is a better choice for classification rather than full feature set.  

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