Search Results

Now showing 1 - 3 of 3

Some of the metrics are blocked by your
consent settings
Rough-mutual feature selection based on min-uncertainty and max-certainty
(2012-01-01)
Foitong, Sombut
;
Pinngern, Ouen
;
Attachoo, Boonwat
Feature selection (FS) plays an important role in pattern recognition and machine learning. FS is applied to dimensionality reduction and its purpose is to select a subset of the original features of a data set which is rich in the most useful information. Most existing FS methods based on rough set theory focus on dependency function, which is based on lower approximation as for evaluating the goodness of a feature subset. However, by determining only information from a positive region but neglecting a boundary region, most relevant information could be invisible. This paper, the maximal lower approximation (Max Certainty) minimal boundary region (Mm Uncertainty) criterion, focuses on feature selection methods based on rough set and mutual infonnation which use different values among the lower approximation information and the information contained in the boundary region. The use of this idea can result in higher predictive accuracy than those obtained using the measure based on the positive region (certainty region) alone. This demonstrates that much valuable information can be extracted by using this idea. Experimental results are illustrated for discrete, continuous, and microarray data and compared with other FS methods in terms of subset size and classification accuracy. key words: rough sets, mutual information, feature selection, boundary region, classification accuracy. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.
Some of the metrics are blocked by your
consent settings
Estimating optimal feature subsets using mutual information feature selector and rough sets
(2009-01-01)
Foitong, Sombut
;
Rojanavasu, Pornthep
;
Attachoo, Boonwat
;
Pinngern, Ouen
Mutual Information (MI) is a good selector of relevance between input and output feature and have been used as a measure for ranking features in several feature selection methods. Theses methods cannot estimate optimal feature subsets by themselves, but depend on user defined performance. In this paper, we propose estimation of optimal feature subsets by using rough sets to determine candidate feature subset which receives from MI feature selector. The experiment shows that we can correct nonlinear problems and problems in situation of two or more combined features are dominant features, maintain an improve classification accuracy. © Springer-Verlag Berlin Heidelberg 2009.
Some of the metrics are blocked by your
consent settings
Rough sets based approach to reduct approximation: RSBARA
(2008-12-01)
Foitong, Sombut
;
Srinil, Phaitoon
;
Pinngern, Ouen
Attribute reduction is the process of choosing a subset of attributes from the original set of attributes forming patterns in a given dataset. The subset should be necessary and sufficient to describe target concepts. Rough set theory has been used as an attribute reduction method with impressive success, but current methods are inadequate at finding optimal reductions. On the other hand, the optimal reducts can be obtained by using the stochastic approaches, but it is not easy because its computational complexity for computing reducts is at least O (NxM<sup>2</sup>), where N is the number of attributes and M is the total number of objects. In this paper, we propose an algorithm which uses rough set theory to approximate the reduct and reduces the required computational effort to O(N<sup>2</sup>xM). Experimentation is carried out, using UCI data, which compares with a particle swarm approach and other deterministic rough set reduction algorithms. The experimental results show that the purposed method is more efficient both accuracy and attribute reduction. © 2008 IEEE.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results