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Three-way decisions model based on rough fuzzy set

Three-way decisions model based on rough fuzzy set

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作者： Zhai, Junhai Zhang, Sufang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China Hebei Branch of China Meteorological Administration Training Centre China Meteorological Administration Baoding China

Three-way decisions model proposed by Yao gives a semantic interpretation of positive region, negative region and boundary region. This model was developed in the framework of classical rough set, the approached target concept is a crisp set, the employed knowledge is a equivalence relation. In this paper, we extend the three-way decisions model to rough fuzzy set. Specifically, the target concept is extended to a fuzzy set, while the used knowledge is also a equivalence relation. An example is given to illustrate the computation processes of the proposed three-way decisions model. The extended model can deal with the problems described by fuzzy decision tables with symbolic-valued conditional attributes and fuzzy decision attributes. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Fuzzy sets

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MULTI-CLASS FUZZY SUPPORT VECTOR machine BASED ON DISMISSING MARGIN

MULTI-CLASS FUZZY SUPPORT VECTOR MACHINE BASED ON DISMISSING...

引用

2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： WEI-YUN YAN QIANG HE Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Department of Mathematics Harbin Institute of Technology Harbin 150001 P.R.China

A new method, multi-class fuzzy support vector machine of dismissing margin (DMFSVM) based on class-center, is proposed aiming at the outliers and noises which appear in the large quantity samples. Compared with traditional SVM, this new method eliminates the sensibility of optimal separating hyperplane. It weeds out some sample points which may not be support vectors. And this will be able to decrease the corresponding optimization problem dimension, reduce the memory and the amount of computation, but increase the training speed. At the same time, the new algorithm adopts fuzzy membership function of decreasing Semi-Cauchy type. The advantages are through regulating parameters of fuzzy factor suitably according to the specific circumstances to make the fuzzy factor of isolated points smaller and the fuzzy factor of support vectors larger relatively. So this method can fit the characteristics of fuzzy classification well.

关键词： Fuzzy support vector machine Multi-class classification Mazimal margin of class-center The method of dismissing margin

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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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Updating Approximations of VPRS Model based on Dominance Relations

Updating Approximations of VPRS Model based on Dominance Rel...

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作者： Jiajia HOU Yan LI Yan ZHAN Liang WANG Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei University

In real world problems, the collected data vary from time to time, and therefore, the approximations of a concept by a variable precision rough set model(VPRS) should be correspondingly updated. This paper focuses on developing incremental method to update set approximations of VPRS based on dominance relations. Under dynamic environments where an object is inserted or deleted, we present the updating principles and then develop the incremental method for updating approximation sets. The related theoretical results are presented with proofs, and illustrative examples are also given to support the effectiveness of the proposed method.

关键词： Variable Precision Rough Set Dominance Relation Set Approximation Dynamic Information Systems Incremental Update

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Incrementally Updating Method in Dominance- Based Rough Set Approach

Incrementally Updating Method in Dominance- Based Rough Set ...

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作者： Yan LI Xiaoqing LIU Jiajia HOU Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

Dominance-based rough set approach(DRSA) can handle the attributes with preference orders, and therefore it has been widely applied in multi-criteria decision making problems. In real applications, the collected information is updated from time to time which results in dynamic information systems, especially when the attributes or objects are inserted or deleted. The traditional DRSA needs to update the set approximations whenever the information systems change, which decreases the method efficiency greatly. For classification problems with multiple criteria, this paper presents incremental algorithms to update set approximations when an object is inserted or deleted, which is expected to be more efficient than computing the approximations from the scratch. The related theoretical results are presented with proofs, and illustrative examples are also given to support the effectiveness of the proposed incremental method.

关键词： Rough Set Dominance Relation Set Approximation Dynamic Information Systems Incremental Update

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A Total Error Rate Multi-class Classification

A Total Error Rate Multi-class Classification

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Xizhao Wang Meng Zhang Shuxia Lu Xu Zhou Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

ISBN: (纸本)9781467317139

The total error rate (TER) has been presented as a minimum classification error model for the single-layer feedforward network (SLFN) learning. The TEE, which uses one-against-all (OAA) for multi-class classification, may cause unbalanced data set especially for large number of training data in multi-class classification and then often has a bad influence on the accuracy. This paper proposes a new method, called multi-class total error rate (MTER) to deal with this problem. The MTER, which uses a unified learning mode of regression and multi-class classification and minimizes the error rate for each class, can approximate any target functions. It implies that a balanced data set can be obtained and the training process can be simplified. Experiments show that MTER has a higher accuracy and lower computational complexity in comparison with some learning algorithms such as ELM and TER. The experiments also show that the MTER has a similar performance with LIBSVM.

关键词： Extreme learning machine One-against-all Tatal error rate Multi-class classification Support vector machine Phyla multi-class Global Error Dataset Error analysis learning algorithms Ecosystem models Elm TECR gene

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A Core Set Weighted Support Vector machines

A Core Set Weighted Support Vector Machines

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作者： Shuxia LU Limin LI Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

Classification for large datasets is a classical problem in machine learning. In this paper, we focus on effevtive classification algorithm for large datasets and imbalanced datasets. First, to deal with imbalanced dataset, we define the weight according to the size of positive and negative dataset. Then, a fast learning algorithm on large datasets called a core set weighted support vector machines(CSWSVM) is proposed. In the proposed approach, the corresponding core set(CS) can be solved by employing the core vector machine(CVM) or generalized CVM(GCVM), and then the weighted support vector machines(WSVM) can be used to implement classification for imbalanced datasets. Experimental results on UCI and USPS datasets demonstrate that the proposed method is effective.

关键词： core vector machine weight support vector machine core set

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A Rough Set Model Based on Extended Dominance Relations

A Rough Set Model Based on Extended Dominance Relations

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作者： Yan LI Shuai SUN Yongfei JIN Yan ZHAN Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University

Dominance relation rough set approach(DRSA) is a useful mathematical tool to deal with preference-ordered data. The main idea is using dominance relations to replace equivalent relations in classical rough set theory. However, the definition of conventional dominance relation is very strict which may limit its application to information systems with relative large number of attributes. In this paper, we relax the conditions in the definition of dominance relation and introduce the concept of extended dominance relation. The proprieties of this new concept are also discussed and it is found that all the properties of classical dominance relation are still satisfied.

关键词： Rough Set Information System Dominance Relation DRSA Extended Dominance Relation

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A Multi-task Pathfinding Method Based on CBR and Kd-tree

A Multi-task Pathfinding Method Based on CBR and Kd-tree

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作者： Yan Li Sen Liang Lanming Su Jiacheng He Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

Pathfinding is a typical task in many computer games, and its performance will affect the quality of game AI. In order to enhance the efficiency of multi-task pathfinding, case-based reasoning has been introduced in traditional A* algorithm, called the CBMT method. The method needs to select representative paths which can cover the whole map to build a compact case base, which is difficult in large maps. Besides, repeatedly searching for similar cases for each pathfinding task would be a time consuming process. To address these problems, we provide a kd-tree case storage structure and case retrieval mechanical in the CBMT method. The pre-stored cases(previously found paths) are generated randomly and incrementally. The original flat storage structure of the cases is changed into the kd-tree structure. Since the searching space can be reduced by branch pruning in case retrieval, the pathfinding efficiency has been improved obviously, and the number of searched nodes is also reduced.

关键词： Multi-task Pathfinding Case-based Reasoning Kd-tree Nearest-neighbors Search Algorithm A*

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

引用

7th IFIP International Conference on Intelligent Information Processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

关键词： MapReduce

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