Simultaneous instance and feature selection for improving prediction in special education data

2017 ◽  
Vol 51 (3) ◽  
pp. 278-297 ◽  
Author(s):  
Yenny Villuendas-Rey ◽  
Carmen Rey-Benguría ◽  
Miltiadis Lytras ◽  
Cornelio Yáñez-Márquez ◽  
Oscar Camacho-Nieto
Keyword(s):  
Feature Selection ◽  
Data Sets ◽  
Feature Selection Technique ◽  
Similarity Functions ◽  
Content Type ◽  
Education Data ◽  
Multiple Feature ◽  
The Family ◽  
Selection For

Purpose The purpose of this paper is to improve the classification of families having children with affective-behavioral maladies, and thus giving the families a suitable orientation. Design/methodology/approach The proposed methodology includes three steps. Step 1 addresses initial data preprocessing, by noise filtering or data condensation. Step 2 performs a multiple feature sets selection, by using genetic algorithms and rough sets. Finally, Step 3 merges the candidate solutions and obtains the selected features and instances. Findings The new proposal show very good results on the family data (with 100 percent of correct classifications). It also obtained accurate results over a variety of repository data sets. The proposed approach is suitable for dealing with non-symmetric similarity functions, as well as with high-dimensionality mixed and incomplete data. Originality/value Previous work in the state of the art only considers instance selection to preprocess the schools for children with affective-behavioral maladies data. This paper explores using a new combined instance and feature selection technique to select relevant instances and features, leading to better classification, and to a simplification of the data.

scholarly journals Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
A. Gaspar-Cunha ◽  
G. Recio ◽  
L. Costa ◽  
C. Estébanez
Keyword(s):  
Feature Selection ◽  
Bankruptcy Prediction ◽  
Data Sets ◽  
Credit Risks ◽  
Multiobjective Approach ◽  
Self Adaptation ◽  
Selection For ◽  
Excellent Tool ◽  
Vast Area

Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier.

Using class-based feature selection for the classification of hyperspectral data

2011 ◽  
Vol 32 (15) ◽  
pp. 4311-4326 ◽  
Author(s):  
Yasser Maghsoudi ◽  
Mohammad Javad Valadan Zoej ◽  
Michael Collins
Keyword(s):  
Feature Selection ◽  
Hyperspectral Data ◽  
Selection For

scholarly journals Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1816
Author(s):  
Hailun Xie ◽  
Li Zhang ◽  
Chee Peng Lim ◽  
Yonghong Yu ◽  
Han Liu
Keyword(s):  
Feature Selection ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Particle Swarm Optimisation ◽  
Data Sets ◽  
Discriminative Feature ◽  
Advanced Search ◽  
Selection For ◽  
Selection Tasks ◽  
New Strategies

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

scholarly journals Feature Selection for Shape-Based Classification of Biological Objects

2003 ◽  
pp. 114-125 ◽  
Author(s):  
Paul Yushkevich ◽  
Sarang Joshi ◽  
Stephen M. Pizer ◽  
John G. Csernansky ◽  
Lei E. Wang
Keyword(s):  
Feature Selection ◽  
Biological Objects ◽  
Selection For

Heuristic Feature Selection for Shaving Tool Wear Classification

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
Yong Wang ◽  
Adam J. Brzezinski ◽  
Xianli Qiao ◽  
Jun Ni
Keyword(s):  
Feature Selection ◽  
Tool Wear ◽  
Tool Condition Monitoring ◽  
Shop Floor ◽  
Feature Selection Technique ◽  
Life Data ◽  
Tool Condition ◽  
Feature Extraction And Selection ◽  
Selection For ◽  
Tool Wear Classification

In this paper, we develop and apply feature extraction and selection techniques to classify tool wear in the gear shaving process. Because shaving tool condition monitoring is not well-studied, we extract both traditional and novel features from accelerometer signals collected from the shaving machine. We then apply a heuristic feature selection technique to identify key features and classify the tool condition. Run-to-life data from a shop-floor application is used to validate the proposed technique.

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