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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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USING 2-ADDITIVE FUZZY MEASURE IN MULTIPLE CLASSIFIER SYSTEM

USING 2-ADDITIVE FUZZY MEASURE IN MULTIPLE CLASSIFIER SYSTEM

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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： AI-XIA CHEN GUO-FANG ZHANG NING LI LI ZHAO GUO-QIANG YUAN Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Department of Basic Courses Hebei College of Finance Baoding 071051 China

Fuzzy measure and integral are widely used in Multiple Classifier System (MCS). But the number of coefficients involved in the fuzzy integral model grows exponentially with the number of classifiers to be aggregated. The main difficulty is to identify all these coefficients. This paper does an attempt Using 2-additrve fuzzy measure in Multiple Classifier System. Our conclusion is that when different interactions exist in different classifiers the complexity of the computation can be significantly reduced by 2-order additive measure.A simple example is included to illustrate the 2-order additive measure.

关键词： Fuzzy measure λ - fuzzy measure 2-additive fuzzy measure Interaction Multiple Classifier System

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THE STUDY OF UNSTABLE CUT-POINT DECISION TREE GENERATION BASED-ON THE PARTITION IMPURITY

THE STUDY OF UNSTABLE CUT-POINT DECISION TREE GENERATION BAS...

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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： XI-ZHAO WANG HUI-QIN ZHAO SHUAI WANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie College of Mathematics and Computer Science Hebei University Baoding 071002 China

This paper is to discuss the reduction of computation complexity in decision tree generation for the numerical-valued attributes. The proposed method is based on the partition impurity. The partition impurity minimization is used to select the expanded attribute for generation the sub-node during the tree growth. After inducing the unstable cut-points of numerical-attributes, it is analytically proved that the partition impurity minimization can always be obtained at the unstable cut-points. It implies that the computation on stable cut-points may not be considered during the tree growth. Since the stable cut-points are far more than unstable cut-points, the experimental results show that the proposed method can reduce the computational complexity greatly.

关键词： Numerical-valued attributes decision trees Gini Indez Information entropy Partition impurity Unstable cut-point

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Imbalanced Data Classification Based on Extreme learning machine Autoencoder

Imbalanced Data Classification Based on Extreme Learning Mac...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Chu Shen Su-Fang Zhang Jun-Hai Zhai Ding-Sheng Luo Jun-Fen Chen Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei China Hebei Branch of China Meteorological Administration Training Center China Meteorological Administration Baoding China Key Lab. of Machine Perception (Ministry of Education) School of EECS Peking University Beijing China

ISBN: (纸本)9781538652152

In practice, there are many imbalanced data classification problems, for example, spam filtering, credit card fraud detection and software defect prediction etc. it is important in theory as well as in application for investigating the problem of imbalanced data classification. In order to deal with this problem, based on extreme learning machine autoencoder, this paper proposed an approach for addressing the problem of binary imbalanced data classification. The proposed method includes 3 steps. (1) the positive instances are used as seeds, new samples are generated for increasing the number of positive instances by extreme learning machine autoencoder, the generated new samples are similar with the positive instances but not same. (2) step (1) is repeated several times, and a balanced data set is obtained. (3) a classifier is trained with the balanced data set and used to classify unseen samples. The experimental results demonstrate that the proposed approach is feasible and effective.

关键词： Classification algorithms Glass Training Measurement Data models Support vector machines Decoding

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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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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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Calibrating Multimodal learning

arXiv

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arXiv 2023年

作者： Ma, Huan Zhang, Qingyang Zhang, Changqing Wu, Bingzhe Fu, Huazhu Zhou, Joey Tianyi Hu, Qinghua College of Intelligence and Computing Tianjin University Tianjin China AI Lab. Tencent Shenzhen China Tianjin Key Lab of Machine Learning Tianjin China Singapore Singapore

Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper, through extensive empirical studies, we identify current multimodal classification methods suffer from unreliable predictive confidence that tend to rely on partial modalities when estimating confidence. Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted. To address the issue, we introduce an intuitive principle for multimodal learning, i.e., the confidence should not increase when one modality is removed. Accordingly, we propose a novel regularization technique, i.e., Calibrating Multimodal learning (CML) regularization, to calibrate the predictive confidence of previous methods. This technique could be flexibly equipped by existing models and improve the performance in terms of confidence calibration, classification accuracy, and model robustness. © 2023, CC BY.

关键词： learning systems

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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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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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