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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： YAN-JIE LI ZHI-PING DIAO JUN-FEN CHEN College of Chinese Literature Hebei University Baoding 071002 Hebei China Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie

Classification is necessary and basic to scientific research. The Chinese loanword has always been a hot spot of studies on Chinese linguistics, however the range of it remained unsettled. The paper will introduce fuzzy set technology into the discriminant process of Chinese loanwords to compose a reliable and efficient classifier. Simulations verify the efficiency and feasibility.

关键词： Classification fuzzy set Membership function Chinese Loanwords Similarity

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FUZZY DECISION TREE BASED ON THE IMPORTANT DEGREE OF FUZZY ATTRIBUTE

FUZZY DECISION TREE BASED ON THE IMPORTANT DEGREE OF FUZZY A...

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2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： XI-ZHAO WANG JUN-HAI ZHAI SU-FANG ZHANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Teaching and research of section of mathematics Hebei Information Engineering School Baoding 0710

There may be many fuzzy attributes in a fuzzy information system. Different fuzzy attribute has different contribution to classification. More important attributes have more contribution than the others to decision-making. In this paper, based on the importance of the fuzzy condition attributes, a new method generating a fuzzy decision tree is proposed, which uses the important degree of the fuzzy condition attribute with respect to the fuzzy decision attributes to select attributes to expand the branches of a fuzzy decision tree. A comparison between the new method and fuzzy ID3 is provided. It is shown that the new method is more efficient than fuzzy ID3.

关键词： Condition attribute Decision attribute Fuzzy attribute Fuzzy decision tree Important degree

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IMAGE RECOGNITION BASED ON WAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORKS

IMAGE RECOGNITION BASED ON WAVELET TRANSFORM AND ARTIFICIAL ...

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2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： JUN-HAI ZHAI LI-JUAN LIU SU-FANG ZHANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Teaching and research of section of mathematics Hebei Information Engineering School Baoding 07100

This paper proposes an image recognition method, which consists of two steps: features extraction based on wavelet transform and image recognition using artificial neural networks. More specifically, wavelet transform is used to decompose the original image into different frequency sub-bands, then a set of features are extracted from the wavelet coefficients. The feature set as input fed into neural network for recognition. The experimental results confirmed effectiveness of the proposed approach.

关键词： Image recognition Wavelet transform Artificial neural networks Multi-scale features

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MULTI-CLASS FUZZY SUPPORT VECTOR machine BASED ON DISMISSING MARGIN

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

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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： 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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Knowledge Updating Method Based On Approximate Reduction Under Dominance Relations

Knowledge Updating Method Based On Approximate Reduction Und...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Yan Li Li Zhang Ting Ting Wu Jun Fen Chen School of Applied Mathematics Beijing Normal University Zhuhai Zhuhai City Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding

ISBN: (纸本)9781538652152

Rough set approach based on dominance- equivalence relation can be used to handle classification problems with preference ordered conditional attributes. It firstly extracts the most important information to the decisions, and then approximates target concepts and finally form the decision-making knowledge. In this paper, we study the issue of updating knowledge in classification problems under dynamic environments. When some samples are inserted or deleted, the update principle of dominating sets is given and the approximate reduction algorithm based on sample selection is developed. The proposed method only needs to individually consider the impact of changing samples on knowledge reduction, and therefore it can obviously reduce the storage space. More importantly, since it can utilize previous calculation results, the time cost of subsequent calculations is thus significantly reduced. Ten UCI data sets are selected in our experiments. The results show the effectiveness and efficiency of the proposed method.

关键词： Rough sets Approximation algorithms Information systems Heuristic algorithms Ducts Classification algorithms

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A new instance selection algorithm based on contribution for nearest neighbour classification

A new instance selection algorithm based on contribution for...

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International Conference on machine learning and Cybernetics

作者： Cai, Yong-Hua Wu, Bo He, Yu-Lin Zhang, Ye Department of Mathematics and Computer Science Hebei Normal University for Nationalities Chengde067000 Hebei China Automation Department Chengde Iron and Steel Branch Hebei Iron and Steel Group Co. Ltd. Chengde 067000 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Nearest Neighbor Classifier is one of the most classical lazy learning schemes. The basic nearest neighbor classifiers suffer from the common problem that the instances used to train the classifier are all stored indiscriminately, and as a result, the required memory storage is huge and response time becomes slow with a large database. In this paper, a new Instances Selection algorithm based on Classification Contribution Function shortly named ISCC is presented. In this algorithm, a function is introduced to evaluate the classification ability of the instances. For each instance, the function considers its contribution to the neighbor instances not only with same class but also with different class. Then an instance with the highest value of Classification Contribution Function is added to the condensed subset and the instances which can be classified correctly are deleted in each iteration. This process is repeated until the subset is no longer getting larger. The time complexity of ISCC is Ο (in2). The experimental results on two artificial databases and some real databases demonstrate the effectiveness and the feasibility of the proposed algorithm. Compared to traditional methods, such as MCS, ICF and ENN, the condensed sets obtained by ISCC is superior in storage and classification accuracy. © 2010 IEEE.

关键词： Classification (of information)

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THE CONCEPT learning IN THE THEORY OF ROUGH SETS

THE CONCEPT LEARNING IN THE THEORY OF ROUGH SETS

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2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： QUN-FENG ZHANG YU-TING JIANG ZHI-QIANG LI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Industrial and commercial college Heibei University Baoding 071002 China Hebei Information Engineering School Baoding 071000 China

Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.

关键词： Rough Set Concept learning Sample Complezity PAC-Learnability

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Efficient image representation for object recognition via pivots selection

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Frontiers of Computer Science 2014年

作者： Xie, Bojun Liu, Yi Zhang, Hui Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well-known pre-defined patch-level descriptors such as scaleinvariant feature transform (SIFT) and histogram of oriented gradient (HOG), the kernel descriptor (KD) method [1] offers a new way to "grow-up" features from a match-kernel defined over image patch pairs using kernel principal component analysis (KPCA) and yields impressive *** this paper, we present efficient kernel descriptor (EKD) and efficient hierarchical kernel descriptor (EHKD), which are built upon incomplete Cholesky decomposition. EKD automatically selects a small number of pivot features for generating patch-level features to achieve better computational efficiency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD. © 2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

关键词： Object recognition

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NC-ALG: Graph-Based Active learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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Selection of deep web database based on query relevance

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Journal of Information and computational Science 2009年第3期6卷 1113-1120页

作者： Yuan, Fang Fan, Jingchuan Liu, Shuang Zhang, Ming College of Mathematics and Computer Science Hebei University Hebei Baoding 071002 China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Hebei Baoding 071002 China College of Quality Technology Supervision Hebei University Hebei Baoding 071000 China

During recent years, there are more and more high-quality information in the Web database. Thus, it is becoming more and more important to find the most relevant Web database to user's query. In this paper, we propose a method to calculate the relevance of Web database with respect to user's query. The method submits frequent query to Web database, classifies the obtained results from the Web database by their characteristic, and calculates the relevance between the query and each type of obtained results together with the proportion of each type of the obtained result occupying all the results from all Web database. Meanwhile, the relevance between the query and the Web database is obtained through the weighted sum of the foresaid relevance and proportion. Experimental results on several actual Web databases demonstrate the effectiveness of the proposed method in evaluating the Web database's supporting power with specific queries. 1548-7741/ Copyright © 2009 Binary Information Press.

关键词： Query languages

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