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Two-stage dimensionality reduction approach based on 2DLDA and fuzzy rough sets technique

NEUROCOMPUTING 2011年第17期74卷 3722-3727页

作者： Zhao, Hao-Xin Xing, Hong-Jie Wang, Xi-Zhao Hebei Univ Coll Math & Comp Sci Key Lab Machine Learning & Computat Intelligence Baoding 071002 Hebei Province Peoples R China

Traditional two-dimensional linear discriminant analysis (2DLDA) can deal with discriminant information between classes and directly extract features from image matrices. However, 2DLDA essentially works solely in the row-direction of images. Therefore, the features extracted by 2DLDA may contain redundant information. In this letter, a dimensionality reduction method based on 2DLDA and fuzzy rough sets technique is proposed to deal with the foresaid problem. Experimental results on the four benchmark face databases demonstrate that the proposed method is superior to its related methods. (C) 2011 Elsevier B.V. All rights reserved.

关键词： 2DLDA Fuzzy rough sets Feature extraction Dimensionality reduction Face recognition

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Robust feature extraction for novelty detection based on regularized correntropy criterion

Robust feature extraction for novelty detection based on reg...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ren, Huan-Ru Xing, Hong-Jie Hebei Univ Coll Math & Comp Sci Key Lab Machine Learning & Computat Intelligence Baoding 071002 Hebei Province Peoples R China

ISBN: (纸本)9781467317146

In this paper, a robust feature extraction method based on regularized correntropy criterion (RCC) is proposed for novelty detection. In RCC, the criterion aims to maximize the difference between the correntropy of the normal data with their mean and the correntropy of the novel data with the mean of normal data. Moreover, the optimal projection vectors in the proposed objective function can be obtained by the half-quadratic (HQ) optimization technique with an iterative manner. Experimental results on one synthetic data set and nine benchmark data sets for novelty detection demonstrate that the proposed method is superior to its related approaches.

关键词： Correntropy half-quadratic optimization feature extraction novelty detection

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Case-based multi-task pathfinding algorithm

Case-based multi-task pathfinding algorithm

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2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： Li, Yan Su, Lan-Ming He, Qiang Key Lab. in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781467314855

Pathfinding is an important task in computer games, where the algorithm efficiency is the key issue. In this paper, we introduce case-based reasoning method in the process of A* algorithm in multi-task pathfinding. Firstly, we save some typical paths as cases. When a new task is coming, it no longer uses A* to find a path from scratch, but firstly computes the similarity of the new task and the stored cases to decide whether to go along the previous found paths or not. A solution to the new task will be obtained after adapting to the found similar case(s). Obviously, this memory-based pathfinding can reduce the search time at the cost of using more memory to store found paths as cases. Through experimental results, it is demonstrated that, as the number of stored paths is increasing, fewer nodes are needed to be searched during the pathfinding process. © 2012 IEEE.

关键词： Case based reasoning

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PSO-based method for learning similarity measure of nominal features

PSO-based method for learning similarity measure of nominal ...

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International Conference on machine learning and Cybernetics

作者： Li, Yan Zhang, Xiu-Li Key Lab. in Machine Learning and Computational Intelligence Faculty of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781424465262

This paper presents a PSO-based method for learning similarity measure of nominal features for case based reasoning classifiers (i.e. CBR classifiers). The symbolic features considered here takes completely unordered values. It has been indicated in [3] that in specific classification task, the similarities between these nominal feature values can not be simply considered as either 0 or 1. A GA-based approach has been developed for learning similarity measure of such feature values. However, when the number of features and feature values become larger, the GA-based algorithm's convergence speed obviously slows down, and the accuracy of classification may be also affected. To address this problem, we propose a PSO-based algorithm for learning similarity measure of nominal features and further describe feature importance through the learned similarity measure. The experimental results show that, using the proposed PSO-based algorithm, the convergence speed is much faster than that of GA-based algorithm and the accuracy is also improved. In addition, we also explain that the feature importance defined through the learned similarities is essentially consistent with that in rough sets, and an illustrative example is finally provided. © 2010 IEEE.

关键词： Particle swarm optimization (PSO)

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Reductions in inconsistent decision systems based on dominance relations

Reductions in inconsistent decision systems based on dominan...

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International Conference on machine learning and Cybernetics

作者： Li, Yan Sun, Na-Xin Zhao, Jin Key Lab. In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781424465262

By incorporating domination principle in inconsistent decision systems based on dominance relations, we define the concept of distribution function for a decision system to directly reflect the inconsistent degree of this system. A new type of distribution reduction and maximum distribution reduction are correspondingly introduced. The relationships between these two reductions are discussed, and their judgment theorems are given. In addition, this article also discusses the relations of several existing reductions, including compatible reduction, absolute reduction, and the proposed distribution reduction and maximum distribution reduction. © 2010 IEEE.

关键词： Distribution functions

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Chinese functional chunk identification based on part of speech

Chinese functional chunk identification based on part of spe...

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International Conference on Communication Systems and Network Technologies, CSNT 2012

作者： Wang, Cheng-Yan Li, Xin-Fu Tian, Xue-Dong College of Mathematics and Computer Science Hebei University Baoding China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province China

ISBN: (纸本)9780769546926

Chinese functional chunk describes the basic skeleton of the Chinese sentences. It is the important bridge for joining syntax and semantic description, and the Chinese functional chunk identification plays a key role in the field of natural language processing. Identifying the functional chunk is mostly based on methods of rules which come from the syntactic analysis. In this paper, the functional chunk is mainly identified according to the effect of the part of speech in the syntactic analysis. The experimental results show the proposed method works well on the functional chunk identification. © 2012 IEEE.

关键词： Semantics

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CET4 passing rate analysis based on fuzzy decision tree induction and active learning

CET4 passing rate analysis based on fuzzy decision tree indu...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Qiao, Qing-Shui Wang, Hai-Tao Wang, Zhen-Yu Zhai, Jun-Hai Dept. of English Hebei Institute of Civil Engineering and Architecture Zhangjiakuo City Hebei China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding City China

ISBN: (纸本)9781457703065

College English Test Band Four (CET4) in China has been a significant impact on evaluating the English preliminary level of a college student or a class. How to improve the college English teaching and go further to raise passing rate of CET4 are a challenge for many colleges and universities. This paper makes an attempt to quantitatively analyze the CET4 and exam-related factors by using fussy decision tree technique and active learning based on uncertainty. Several features are selected to formulate this problem. The weighted margin is proposed as the new uncertainty measure criterion for unlabeled instance, and a density measure is introduced for avoiding selecting isolated instances. Experiments and simulations on different classes of students show the proposed quantitative analysis method is feasible and effective, which can provide teachers with some useful guidelines for how to improve the college English teaching. © 2011 IEEE.

关键词： Decision trees

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Optimization methods for resources allocation in real-time strategy games

Optimization methods for resources allocation in real-time s...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Tong, Xiao-Lei Li, Yan Li, Wen-Liang Zhang, Lei Key Lab. In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China Handan Hospital of Traditional Chinese Medicine Handan 056000 China

ISBN: (纸本)9781457703065

In order to meet the demands of the real time strategy (RTS) games, two learning methods are proposed based on genetic algorithm (GA) and Particle swarm optimization (PSO) to handle the problem of multi-team weapon target assignment (MT-WTA) and distribution of defensive position with restrictive limit of weapon resource. The goal is to take the greatest destruction on the targets. Firstly, we use GA to assign different types of weapons under limited resources. Secondly, we put the optimal results from the first stage into a random game's map to obtain final defensive locations. Then GA and PSO are used to achieve the best distribution of defensive positions respectively and their performance is compared in RTS Games. Both of these two methods have provided efficient, interesting AI to solve real-time strategy games problems, the experimental results can well support this point. © 2011 IEEE.

关键词： Genetic algorithms

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A Feature Extraction Method Using Base Phrase and keyword In Chinese Text

A Feature Extraction Method Using Base Phrase and keyword In...

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2008 3rd International Conference on Intelligent System and Knowledge Engineering(第三届智能系统与知识工程国际会议)(ISKE 2008)

作者： Xin-fu LI lei-lei ZHAO li-hong WU College of Mathematics and Computer Science Hebei UniversityBaoding071002HebeiChina Key Lab of Machine Learning and Computation Intelligence of Hebei Province

The feature extraction is the most key technologyof text *** word is used as the feature in the traditional text classification,and its effect forthe text classification is *** featureextraction method using base phrase and keywordchanges the feature extraction of Chinese text fromsyntax and semantic *** the first,analyzing thefeature of baseNP and basedVP,and then make somewords into baseNP and baseVP which accord to therules of phrase,give WSD to other words in the *** paper proposes a stepwise feature extraction fromword to *** experiment results show that thismethod can perform much better than traditionalfeature extraction method it can improve the textclassification precision and recall.

关键词： Feature extraction Text categorization Frequency Support vector machines Support vector machine classification Intelligent systems learning systems Competitive intelligence Knowledge engineering Tagging

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A fast positive-region reduction method based on dominance-equivalence relations

A fast positive-region reduction method based on dominance-e...

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2016 International Conference on machine learning and Cybernetics, ICMLC 2016

作者： Jin, Yongfei Li, Yan He, Qiang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei Province071002 China School of Science Beijing University of Civil Engineering and Architecture Beijing102616 China

ISBN: (纸本)9781509003891

In this paper, we consider decision systems which consist of preference ordered conditional attributes and symbolic decision attributes. Thus, dominance relations and equivalence relations can be respectively defined on the conditional attribute set and decision attribute set. Based on this combined dominance-equivalence relations, a fast positive-region reduction (FPR) algorithm is developed by introducing the concept of positive approximation together with positive region reduction. The computation load of attribute reduction has been greatly reduced when compared with the original positive-region reduction algorithm (PR). Finally, the experimental results on 9 UCI data are demonstrated to make comparisons between the PR and the FPR method. © 2016 IEEE.

关键词： Approximation algorithms

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