A Heuristic Algorithm for Feature Selection Based on Optimization Techniques

2011 ◽  
pp. 13-26 ◽  
Author(s):  
A. M. Bagirov ◽  
A. M. Rubinov ◽  
J. Yearwood
Keyword(s):  
Feature Selection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Numerical Experiments ◽  
Optimization Techniques ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Selection Of

The feature selection problem involves the selection of a subset of features that will be sufficient for the determination of structures or clusters in a given dataset and in making predictions. This chapter presents an algorithm for feature selection, which is based on the methods of optimization. To verify the effectiveness of the proposed algorithm we applied it to a number of publicly available real-world databases. The results of numerical experiments are presented and discussed. These results demonstrate that the algorithm performs well on the datasets considered.

scholarly journals Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Hong Zhao ◽  
Fan Min ◽  
William Zhu
Keyword(s):  
Feature Selection ◽  
Measurement Errors ◽  
Heuristic Algorithms ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Numeric Data ◽  
New Research ◽  
The Cost ◽  
Pruning Techniques ◽  
Selection Of

Feature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric data with measurement errors. The major contributions of this paper are fourfold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. It is distinguished from the existing models mainly on the error boundaries. Second, a covering-based rough set model with normal distribution measurement errors is constructed. With this model, coverings are constructed from data rather than assigned by users. Third, a new cost-sensitive feature selection problem is defined on this model. It is more realistic than the existing feature selection problems. Fourth, both backtracking and heuristic algorithms are proposed to deal with the new problem. Experimental results show the efficiency of the pruning techniques for the backtracking algorithm and the effectiveness of the heuristic algorithm. This study is a step toward realistic applications of the cost-sensitive learning.

scholarly journals Scatter Search for the Feature Selection Problem

2004 ◽  
pp. 517-525 ◽  
Author(s):  
Félix C. García López ◽  
Miguel García Torres ◽  
José A. Moreno Pérez ◽  
J. Marcos Moreno Vega
Keyword(s):  
Feature Selection ◽  
Scatter Search ◽  
Selection Problem ◽  
Feature Selection Problem
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