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International Conference on machine learning and Cybernetics (ICMLC)

作者： Yan Li Xiu-Li Zhang Key Laboratory In Machine Learning and Computational Intelligence Faculty of Mathematics and Computer Science Hebei University Baoding Hebei China

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 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.

关键词： Classification algorithms Accuracy Particle measurements Atmospheric measurements Image color analysis machine learning Convergence

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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 (ICMLC)

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

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.

关键词： Information systems Distribution functions Approximation methods machine learning Cybernetics Set theory

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Selection of Deep Web Database Based on Retrieval Performance

Selection of Deep Web Database Based on Retrieval Performanc...

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International Workshop on Education Technology and Computer Science, ETCS

作者： Weijing Li Fang Yuan Ming Zhang Key Laboratory in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9781424463886;9780769539874

A mass of high-quality information included in Deep Web can be accessed, which is still growing rapidly with the rapid development of the World Wide Web. Therefore it becomes more and more important to find the Web databases which are most relevant to the user queries. In this paper we propose a selection method of Web database based on retrieval performance. This method can fix the topic based on website characteristics and then classify the websites. Finally it decides which Web database can be chosen based on the retrieval performance. This method can not only accurately select the Web databases which satisfy the user queries but also improve the speed of the database query and the quality of retrieval.

关键词： Databases Information retrieval Books Computer science Costs Content based retrieval Engines Computer science education Educational technology machine learning

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A text classification method based on the improved CR-tree and the weighted association rule

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Journal of computational Information Systems 2009年第6期5卷 1577-1584页

作者： Wang, Yu Zhao, Yao Yuan, Fang College of Mathematics and Computer Science Hebei University Baoding 071002 China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding 071002 China

Classification based on association rules is a common and easily understand algorithm for text classification. To improve its classification accuracy, the key is to generate more effective rules. Sometimes, it will overdraw the role of some training texts. To avoiding this and generate more effective rules, this paper defines the Classification Path and proposes a new text classifier (CP-tree) based on CR-tree. When a new text is coming, to avoid overdrawing, association rules are generated through scanning Classification Path;in addition, to make the role of association rules in class prediction more reasonable, the association rules and weight for the new text are obtained not only according to the training texts, but also the new texts. Experimental results show that the accuracy of text classification is improved with the algorithm based on CP-tree. 1553-9105/ Copyright © 2009 Binary Information Press.

关键词： Classification (of information)

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A three-stage method for classification of binary imbalanced big data 19

A three-stage method for classification of binary imbalanced...

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19th International Conference on machine learning and Cybernetics, ICMLC 2020

作者： Zhai, Jun-Hai Zhang, Su-Fang Wang, Mo-Han Li, Yan Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Baoding071002 China China Meteorological Administration Hebei Branch of China Meteorological Administration Training Center Baoding071000 China Beijing Normal University School of Applied Mathematics Zhuhai519087 China

ISBN: (纸本)9780738124261

In the real world, there are many imbalanced data classification problems, such as extreme weather prediction, software defect prediction, machinery fault diagnosis, spam filtering, etc. It has important theoretical and practical value to study the problem of imbalanced data classification. In the framework of binary imbalanced data classification, a three-stage method for classification of binary imbalanced big data was proposed in this paper. Specifically, in the first stage, the negative class big data was clustered into K clusters by K-means algorithm on Hadoop platform. In the second stage, we use instance selection method to select important samples from each cluster in parallel, and obtain K negative class subsets. In the third stage, we first construct K balanced training sets which consist of negative class subset and positive class subset, and then train K classifiers, and finally we integrate these classifiers to classify the unseen samples. Some experiments are conducted to compare the proposed method with two state-of-the-art methods on G-means. The experimental results demonstrate that the proposed method is more effective and efficient than the compared approaches. © 2020 IEEE.

关键词： Big data

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Class Prototype Angle Loss for Imbalanced Data

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SSRN 2023年

作者： Ma, Tingting Lu, Shuxia Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China

Classification problems in the real world are typically characterized by an imbalanced distribution, which poses a challenge to the induction of these labels. In this study, we analyze softmax cross-entropy (CE) and reasons for poor performance of models trained on imbalanced data using cross entropy from a mathematical point of view. Then, we provide a novel explanation for the model’s poor performance on imbalanced data. In particular, the model’s poor performance on imbalanced data is related to CE limitation, which results in the insufficiently compact intraclass features of the trained model and its poor feature extraction ability. We have analyzed the causes of imbalanced problems from a mathematical point of view as well as proposed class prototype angle and class prototype cosine losses, along with new strategies to address these challenges. These two losses improve the model’s performance by increasing the angle interval between classes and focusing on difficult samples of minority classes, thus improving the feature extraction ability of minority classes. The experiment on the datasets is completely consistent with our theoretical view and confirms the superior performance of our method, effectively alleviating the problem of data overfitting. © 2023, The Authors. All rights reserved.

关键词： Image classification

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A method for person name disambiguation based on Baidu Encyclopedia

A method for person name disambiguation based on Baidu Encyc...

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International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE)

作者： Xinfu Li Wenxue Cao Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

The phenomenon of person name ambiguity is widespread on web pages in that one name may be used by different people. It is important to uniquely identify the given person on the web. In this paper, the method Baidu-PND is proposed by the authors. It is an unsupervised name disambiguation method based on Baidu Encyclopedia. We extract three features including background knowledge, contextual feature and Related-Set of the characters from the online Baidu Encyclopedia. The weights of the features are studied by logistic regression algorithm. Then we make a linear fusion of the features. The maximum combined value is selected as the correct person on web pages. Experiments are conducted to measure the performance of Baidu-PND, which show that the performance is higher than we expected, validating its feasibility and effectiveness for person name disambiguation on web pages. And, Baidu-PND is a new method for knowledge mining based on Baidu Encyclopedia.

关键词： Encyclopedias Web pages Feature extraction Context Physics Accuracy Educational institutions

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Feature selection for big data based on mapreduce and voting mechanism 19

Feature selection for big data based on mapreduce and voting...

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19th International Conference on machine learning and Cybernetics, ICMLC 2020

作者： Zhang, Su-Fang Zhai, Jun-Hai Tian, Shi Zhou, Xiang Li, Yan China Meteorological Administration Hebei Branch of China Meteorological Administration Training Center Baoding071000 China Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Baoding071002 China Beijing Normal University School of Applied Mathematics Zhuhai519087 China

ISBN: (纸本)9780738124261

With the rapid development of computer network technology and wireless sensor technology, as well as the arrival of the era of big data, the dimension and sample number of data are growing rapidly. Accordingly, it is important to investigate the problem of feature selection for big data and to design feature selection algorithm for big data. Based on MapReduce and voting mechanism, a feature selection method for big data is proposed in this paper. The proposed methods include three steps: Firstly, partition big data set into m subsets, and deploy the subsets to m computing nodes of Hadoop. Secondly, on the m computing nodes, we employ a feature selection algorithm based on genetic algorithm to select important features in parallel using local data subset, and obtain m feature subsets. Finally, for each feature, m feature subsets are used to vote on it, and the final feature subset is selected according to the voting results. Experimental results on four big data sets demonstrate that the proposed method is effective and efficient. © 2020 IEEE.

关键词： Genetic algorithms

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The application of decision tree in Chinese email classification

The application of decision tree in Chinese email classifica...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Hao Chen Yan Zhan Yan Li Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

Email is a kind of semi-structured document, some important attributes are contained in its structure, and especially using spam-specific features could improve the email classification results. In this paper, we apply decision tree data mining technique to dig out the potential association rules among these attributes of email, and then to identify unknown email's category based on these rules. According to the experiment of applying numerous Chinese emails to our email classifier, the efficiency of our method is not lower than that of other existing methods of checking whole email content text. Meanwhile our method can reduce the cost of computation and consumption of system resources.

关键词： Electronic mail Postal services Classification algorithms machine learning Classification tree analysis Association rules

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Rotation Based Ensemble of One-Class Support Vector machines

Rotation Based Ensemble of One-Class Support Vector Machines

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Wei-Tao Liu Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9781538652152

One-class support vector machine (OCSVM) is regarded as an important one-class classification method for tackling the problem of extreme class imbalance. However, combining several OCSVMs by the traditional ensemble approaches may not improve the performance of the single OCSVM because it is known as a strong classifier. In the paper, a rotation based ensemble method is proposed for integrating OCSVMs., The training data of different OCSVMs in the ensemble is transformed by different rotation matrices. Therefore, the diversity of training data for the ensemble of OCSVMs can be guaranteed. Experiments conducted on ten benchmark data sets to validate the effectiveness of the proposed ensemble strategy.

关键词： Training Support vector machines Training data Bagging Principal component analysis Forestry Diversity reception

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