Search Results

Now showing 1 - 4 of 4

Some of the metrics are blocked by your
consent settings
Min-uncertainty & max-certainty criteria of neighborhood rough-mutual feature selection
(2017-01-01)
Foithong, Sombut
;
Srinil, Phaitoon
;
Pinngern, Ouen
;
Attachoo, Boonwat
Feature Selection (FS) is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Most existing FS methods based on rough set theory use the dependency function for evaluating the goodness of a feature subset. However, these FS methods may unsuccessfully be applied on dataset with noise, which determine only information from a positive region but neglect a boundary region. This paper proposes a criterion of the maximal lower approximation information (Max-Certainty) and minimal boundary region information (Min-Uncertainty), based on neighborhood rough set and mutual information for evaluating the goodness of a feature subset. We combine this proposed criterion with neighborhood rough set, which is directly applicable to numerical and heterogeneous features, without involving a discretization of numerical features. Comparing it with the rough set based approaches, our proposed method improves accuracy over various experimental data sets. Experimental results illustrate that much valuable information can be extracted by using this idea. This proposed technique is demonstrated on discrete, continuous, and heterogeneous data, and is compared with other FS methods in terms of subset size and classification accuracy.
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.
Some of the metrics are blocked by your
consent settings
Towards adapting XCS for imbalance problems
(2008-12-01)
Nguyen, Thach Huy
;
Foitong, Sombut
;
Srinil, Phaitoon
;
Pinngern, Ouen
The class imbalance problem has been recognized as a crucial problem in machine learning and data mining. Learning systems tend to be biased towards the majority class and thus have poor performance in classifying the minority class instances. This paper analyzes the imbalance problem in accuracy-based learning classifier system XCS. XCS has shown excellent performance on some data mining tasks, but as other classifiers, it also performs poorly on imbalance data problems. We analyze XCS's behavior on various imbalance levels and propose an appropriate parameter tuning to improve performance of the system. Particularly, XCS is adapted to eliminate over-general classifiers and protect accurate classifiers of minority class. Experimental results in Boolean function problems show that, with proposal adaptations, XCS is robust to class imbalance. © 2008 Springer Berlin Heidelberg.
Some of the metrics are blocked by your
consent settings
Using a Learning Classifier System for clustering
(2006-12-01)
Tamee, Kreangsak
;
Bull, Larry
;
Pinngern, Ouen
;
Rojanavasu, Pornthep
;
Srinil, Phaitoon
This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of datasets. © 2006 IEEE.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results