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Min-uncertainty & max-certainty criteria of neighborhood rough-mutual feature selection
(2017-01-01)
Foithong, Sombut
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Srinil, Phaitoon
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Pinngern, Ouen
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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.
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Feature subset selection wrapper based on mutual information and rough sets
(2012-01-01)
Foithong, Sombut
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Pinngern, Ouen
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Attachoo, Boonwat
In this paper, we introduced a novel feature selection method based on the hybrid model (filter-wrapper). We developed a feature selection method using the mutual information criterion without requiring a user-defined parameter for the selection of the candidate feature set. Subsequently, to reduce the computational cost and avoid encountering to local maxima of wrapper search, a wrapper approach searches in the space of a superreduct which is selected from the candidate feature set. Finally, the wrapper approach determines to select a proper feature set which better suits the learning algorithm. The efficiency and effectiveness of our technique is demonstrated through extensive comparison with other representative methods. Our approach shows an excellent performance, not only high classification accuracy, but also with respect to the number of features selected. © 2011 Elsevier Ltd. All rights reserved.
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Rough-mutual feature selection based on min-uncertainty and max-certainty
(2012-01-01)
Foitong, Sombut
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Pinngern, Ouen
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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.
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Growing rule-based induction system
(2009-11-12)
Rojanavasu, Pornthep
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Attachoo, Boonwat
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Pinngern, Ouen
Learning Classifier Systems (LCSs) are rule-based systems that have widely been used in data mining over the last few years. This paper employs UCS, a supervised learning classifier system, that was a version of LCSs for classification in data mining tasks. In this paper, we propose an adaptive framework of a rule-based competitive learning environment. In this framework, a growing neural gas (GNG) is used to adaptively cluster the data instances as they arrive. Each instance is then assigned to based classifier, the UCS responsible for the corresponding cluster. Through this mechanism, the complexity of a classification problem is decomposed adaptively into subproblems, each with a lower or equal complexity to the overall problem. Since each instance is exposed to a smaller population size than the single population approach, the throughput of the system increases. The experiments show that the proposed framework can decompose a problem adaptively into several subproblems. The accuracy rate of UCS in the distributed environment can also be better than the normal environment. © 2009 IEEE.
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Effects of distance between classes and training dataset size on imbalance datasets
(2009-04-13)
Huy, Thach Nguyen
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Foitong, Sombut
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Udomthanapong, Sornchai
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Pinngern, Ouen
This paper analyzes the effects of distance between classes and training datasets size to XCS classifier system on imbalanced datasets. Our purpose is to answer the question whether the loss of performance incurred by the classifier faced with class imbalance problems stems from the class imbalance per se or it can be explained in some other ways. The experiments from 250 artificial imbalanced datasets show that XCS can perform well in some imbalance domains if the training datasets size is large enough and the distance between classes is appropriate. Thus, it dose not seem fair to correlate imbalance datasets directly to the loss performance of XCS. Through this research, we also know what kinds of datasets are suitable for training XCS and dealing with class imbalances alone will not always help improve performance of classifiers. © 2009 American Institute of Physics.
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A self-organized, distributed, and adaptive rule-based induction system
(2009-02-12)
Rojanavasu, Pornthep
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Dam, Hai Huong
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Abbass, Hussein A.
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Lokan, Chris
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Pinngern, Ouen
Learning classifier systems (LCSs) are rule-based inductive learning systems that have been widely used in the field of supervised and reinforcement learning over the last few years. This paper employs sUpervised Classifier System (UCS), a supervised learning classifier system, that was introduced in 2003 for classification tasks in data mining. We present an adaptive framework of UCS on top of a self-organized map (SOM) neural network. The overall classification problem is decomposed adaptively and in real time by the SOM into subproblems, each of which is handled by a separate UCS. The framework is also tested with replacing UCS by a feedforward artificial neural network (ANN). Experiments on several synthetic and real data sets, including a very large real data set, show that the accuracy of classifications in the proposed distributed environment is as good or better than in the nondistributed environment, and execution is faster. In general, each UCS attached to a cell in the SOM has a much smaller population size than a single UCS working on the overall problem; since each data instance is exposed to a smaller population size than in the single population approach, the throughput of the overall system increases. The experiments show that the proposed framework can decompose a problem adaptively into subproblems, maintaining or improving accuracy and increasing speed. © 2009 IEEE.
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Estimating optimal feature subsets using mutual information feature selector and rough sets
(2009-01-01)
Foitong, Sombut
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Rojanavasu, Pornthep
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Attachoo, Boonwat
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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.
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Rough sets based approach to reduct approximation: RSBARA
(2008-12-01)
Foitong, Sombut
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Srinil, Phaitoon
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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.
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Cost-sensitive XCS classifier system addressing imbalance problems
(2008-12-01)
Thach, Nguyen Huy
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Rojanavasu, Porntep
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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 generalization for the minority class instances. This paper analyses the imbalance problem in accuracy-based learning classifier systems. In particular, we propose a novel approach based on XCS classifier system and cost-sensitive learning. In our approach, the reward value of correctly identifying the positive (rare) class outweighs the value of correctly identifying the common class. This research provides guidelines to set reward base on the dataset imbalance ratio and a method to calculate reward online base on the information collected by XCS during training is also proposed. Experimental results on synthetic and real-life datasets show that, with appropriate reward settings, XCS is robust to class imbalances. © 2008 IEEE.
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Towards adapting XCS for imbalance problems
(2008-12-01)
Nguyen, Thach Huy
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Foitong, Sombut
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Srinil, Phaitoon
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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.

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