scholarly journals Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

2014 ◽  
Vol 32 (3) ◽  
pp. 369-390 ◽  
Author(s):  
Sana Ben Hamida ◽  
Wafa Abdelmalek ◽  
Fathi Abid
Keyword(s):  
Genetic Programming ◽  
Implied Volatility ◽  
Subset Selection ◽  
Selection Methods ◽  
Training Subset

scholarly journals Electrode subset selection methods for an EEG-based P300 brain-computer interface

2014 ◽  
Vol 10 (3) ◽  
pp. 216-220 ◽  
Author(s):  
Michael T. McCann ◽  
David E. Thompson ◽  
Zeeshan H. Syed ◽  
Jane E. Huggins
Keyword(s):  
Brain Computer Interface ◽  
Subset Selection ◽  
Computer Interface ◽  
Selection Methods

scholarly journals Training subset selection in Hourly Ontario Energy Price forecasting using time series clustering-based stratification

2015 ◽  
Vol 156 ◽  
pp. 268-279 ◽  
Author(s):  
Karol Lina López ◽  
Christian Gagné ◽  
Germán Castellanos-Dominguez ◽  
Mauricio Orozco-Alzate
Keyword(s):  
Time Series ◽  
Subset Selection ◽  
Energy Price ◽  
Price Forecasting ◽  
Time Series Clustering ◽  
Training Subset

Bayesian Subset Selection Methods for Finding Engineering Design Values: an Application to Lumber Strength

Sankhya A ◽  
2018 ◽  
Vol 80 (S1) ◽  
pp. 146-172
Author(s):  
Yumi Kondo ◽  
James V Zidek ◽  
Carolyn G Taylor ◽  
Constance van Eeden
Keyword(s):  
Engineering Design ◽  
Subset Selection ◽  
Selection Methods ◽  
Design Values

On the Stability of Feature Selection Methods in Software Quality Prediction: An Empirical Investigation

2015 ◽  
Vol 25 (09n10) ◽  
pp. 1467-1490 ◽  
Author(s):  
Huanjing Wang ◽  
Taghi M. Khoshgoftaar ◽  
Naeem Seliya
Keyword(s):  
Feature Selection ◽  
Software Quality ◽  
Software Metrics ◽  
Subset Selection ◽  
Feature Subset ◽  
Selection Methods ◽  
Statistical Measures ◽  
Quality Modeling ◽  
The Stability ◽  
Stable Feature

Software quality modeling is the process of using software metrics from previous iterations of development to locate potentially faulty modules in current under-development code. This has become an important part of the software development process, allowing practitioners to focus development efforts where they are most needed. One difficulty encountered in software quality modeling is the problem of high dimensionality, where the number of available software metrics is too large for a classifier to work well. In this case, many of the metrics may be redundant or irrelevant to defect prediction results, thereby selecting a subset of software metrics that are the best predictors becomes important. This process is called feature (metric) selection. There are three major forms of feature selection: filter-based feature rankers, which uses statistical measures to assign a score to each feature and present the user with a ranked list; filter-based feature subset evaluation, which uses statistical measures on feature subsets to find the best feature subset; and wrapper-based subset selection, which builds classification models using different subsets to find the one which maximizes performance. Software practitioners are interested in which feature selection methods are best at providing the most stable feature subset in the face of changes to the data (here, the addition or removal of instances). In this study we select feature subsets using fifteen feature selection methods and then use our newly proposed Average Pairwise Tanimoto Index (APTI) to evaluate the stability of the feature selection methods. We evaluate the stability of feature selection methods on a pair of subsamples generated by our fixed-overlap partitions algorithm. Four different levels of overlap are considered in this study. 13 software metric datasets from two real-world software projects are used in this study. Results demonstrate that ReliefF (RF) is the most stable feature selection method and wrapper based feature subset selection shows least stability. In addition, as the overlap of partitions increased, the stability of the feature selection strategies increased.

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