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ON THE APPLICATION OF ROUGH SETS TO DATA MINING IN ECONOMIC PRACTICE

ON THE APPLICATION OF ROUGH SETS TO DATA MINING IN ECONOMIC ...

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

作者： QUN-FENG ZHANG SU-YUN ZHAO YUN-CHAO BAI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie College of Economics Hebei University Baoding 071002 China

Mathematical models play an important role in the studies of modern economics. But in many fields of economics, it is difficult to build mathematical models for complex phenomena. So data mining is getting more and more popular in discovering the potential pattern of economic knowledge from databases. As a powerful tool for data mining, rough set theory has been widely used. In this research, we draw guidelines from several cases of rough set application in economic practice. Furthermore, to avoid the drawbacks of the existing methods, we develop a methodology for rough analysis in economic sector by combining the advantages of the fuzzy variable precision rough set model.

关键词： Data mining Rough sets Reducts Fuzzy variable precision rough sets

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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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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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Variable-precision Rough Set Approach based on Extended Dominance Relations

Variable-precision Rough Set Approach based on Extended Domi...

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

According to the definition of dominance relation, an object x is said to dominate another object y only when x dominates y on all attributes, which is too strict especially when the number of attributes is large. To cope with this problem, the extended dominance-based rough set model has been developed by introducing a parameter to the concept of traditional dominance relationship in the reported literature. However, in this extended model, the definitions of lower and upper approximations are the same to the traditional model, which may affect the decision making process. In this paper, we introduce the idea of variable precision to the extended dominance rough set model for better fault tolerance ability. The impact of the parameter on decision results with respect to testing accuracy is studied. Finally, an example is given and the experimental results on UCI data are also shown to support the effectiveness of the proposed method.

关键词： Extended dominance relation Variable precision rough set Dominant sets Approximation sets

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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 Fast Algorithm for Computing Dominance Classes

A Fast Algorithm for Computing Dominance Classes

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作者： Yan LI Qun YU Key Lab.Of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University

Traditional rough set theory(TRS) is based on the concept of equivalence relation to define upper and lower approximation sets of a given target concept, and therefore uncertainties in information systems can be represented. By using equivalence relations, TRS only considers whether attribute values are distinguished or not, regardless of the preference information contained in attribute values. Rough sets based on dominance relations effectively solve this problem and can deal with preference-ordered data. In these dominance-based approaches, the computational cost of the dominance classes greatly affects the efficiency of attribute reduction and rule extraction. This paper presents an efficient method of computing dominance classes in an ordered information system by rapidly reducing the search space. Based on the definition of dominance class, the inferior class of an object is gradually removed from the universe with the increase of the attributes in the computation process. Experiments on ten UCI data sets show that the proposed algorithm obviously improves the efficiency of computing dominance classes, especially for large-scale data.

关键词： Rough set dominance class ordered information systems fast algorithm

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

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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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MINING THE HOTTEST TOPICS ON CHINESE WEBPAGE BASED ON THE IMPROVED K-MEANS PARTITIONING

MINING THE HOTTEST TOPICS ON CHINESE WEBPAGE BASED ON THE IM...

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

作者： YU WANG YA-HUI XI LIANG WANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science HebeiUniversity Baoding 071002 China

This paper presents a new method for the mining the hottest topics on Chinese webpage which is based on the improved k-means partitioning algorithm. The dictionary applied to word segmentation is reduced by deleting words which are useless for clustering, and the dictionary tree is created to be applied to word segmentation. Then the speed of word segmentation is improved. Correspondence between words and integers is created by coding words. Then the title is expressed by integer set, and the cost of space and time for clustering is decreased largely. Determining the value of k is a shortcoming of stream data mining based on k-means. By this new method, the value of k is adjusted in clustering. Then both the accuracy and the speed are improved.

关键词： k-means partitioning Data stream Word segmentation Mining the hottest topics

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Local, Mid-Level and Convolutional Features Fusion Using Multiple Kernel learning for Image Classification

Local, Mid-Level and Convolutional Features Fusion Using Mul...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Yao Lu Hui Zhang Bojun Xie Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.

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Unsupervised feature selection based on feature relevance

Unsupervised feature selection based on feature relevance

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Feng Zhang Ya-Jun Zhao Jun-Fen Key Lab. of Machine Learning and computational Intelligence Hebei University Baoding China College of Physics Science and Technology Hebei University Baoding China Key Lab. of Machine Learning and computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

Feature selection is an essential technique used in data mining and machine learning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is rarely studied. In this paper, we proposed an approach to select features for unsupervised problems. Firstly, the original features are clustered according to their relevance degree defined by mutual information. And then the most informative feature is selected from each cluster based on the contribution-information of each feature. The experimental results show that the proposed method can match some popular supervised feature selection methods. And the features selected by our method do include most of the information hidden in the overall original features.

关键词： machine learning Mutual information Random variables Entropy Unsupervised learning Information theory Cybernetics Educational institutions Data mining computational intelligence

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