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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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3-Lie bialgebras and 3-pre-lie algebras induced by involutive derivations

arXiv

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arXiv 2019年

作者： Bai, Ruipu Hou, Shuai Kang, Chuangchuang College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province Baoding071002 China College of Mathematics and Information Science Hebei University Baoding071002 China

In this paper, we study the structure of 3-Lie algebras with involutive derivations. We prove that if A is an m-dimensional 3-Lie algebra with an involutive derivation D, then there exists a compatible 3-pre-Lie algebra (A, -⋯ }D) such that A is the sub-adjacent 3-Lie algebra, and there is a local cocycle 3-Lie bialgebraic structure on the 2m-dimensional semi- direct product 3-Lie algebra A ad∗ A∗, which is associated to the adjoint representation (A, ad). By means of involutive derivations, the skew-symmetric solution of the 3-Lie classical Yang- Baxter equation in the 3-Lie algebra A ad∗ A∗, a class of 3-pre-Lie algebras, and eight and ten dimensional local cocycle 3-Lie bialgebras are constructed. Copyright © 2019, The Authors. All rights reserved.

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3-lie-rinehart algebras

arXiv

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arXiv 2019年

作者： BAI, RUIPU LI, XIAOJUAN WU, YINGLI College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province Baoding071002 China College of Mathematics and Information Science Hebei University Baoding071002 China

In this paper, we define a class of 3-algebraswhich are called 3-Lie-Rinehart algebras. A 3-Lie-Rinehart algebra is a triple (L, A, ρ), where A is a commutative associative algebra, L is an A-module, (A, ρ) is a 3-Lie algebra L-module and ρ(L, L) ⊆ Der(A). We discuss the basic structures, actions and crossed modules of 3-Lie-Rinehart algebras and construct 3-Lie-Rinehart algebras from given algebras, we also study the derivations from 3-Lie-Rinehart algebras to 3-Lie A-algebras. From the study, we see that there is much difference between 3-Lie algebras and 3-Lie-Rinehart algebras. Copyright © 2019, The Authors. All rights reserved.

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MANIN TRIPLES OF 3-LIE ALGEBRAS INDUCED BY INVOLUTIVE DERIVATIONS

arXiv

引用

arXiv 2019年

作者： Hou, Shuai Bai, Ruipu College of Mathematics and Information Science Hebei University Baoding071002 China College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province Baoding071002 China

For any n-dimensional 3-Lie algebra A over a field of characteristic zero with an involutive derivation D, we investigate the structure of the 3-Lie algebra B1 = A ad∗ A∗ associated with the coadjoint representation (A∗, ad∗). We then discuss the structure of the dual 3-Lie algebra B2 of the local cocycle 3-Lie bialgebra (A ad∗ A∗, ∆). By means of the involutive derivation D, we construct the 4n-dimensional Manin triple (B1 B2, [·, ·, ·]1, [·, ·, ·]2, B1, B2) of 3-Lie algebras, and provide concrete multiplication in a special basis Π1 ∪ Π2. We also construct a sixteen dimensional Manin triple (B, [·, ·, ·]) with dim B1 = 12 using an involutive derivation on a four dimensional 3-Lie algebra A with dim A1 = 2. Copyright © 2019, The Authors. All rights reserved.

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AN INSTANCE SELECTION ALGORITHM BASED ON CONTRIBUTION

AN INSTANCE SELECTION ALGORITHM BASED ON CONTRIBUTION

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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 NING ZHANG TAO XIAO Key Lab.for Machine Learning and Computational Intelligence College of Mathematics and Computer Sci College of Science Agricultural University of Hebei Baoding 071002 China

This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and allowing certain error rate on the training set, a condensed set with high condensing rate and satisfying prediction accuracy is obtained. The condensed set is order-independent of the training instances and insensitive to noise. Comparative experiments have been conducted on real data sets, and the results show its superiority to MCS and FCNN in terms of condensing rate and prediction accuracy.

关键词： Instance selection nearest neighbor rule condensed set MCS FCNN

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A DYNAMIC FUZZY MEASURE FOR MULTIPLE CLASSIFIER FUSION

A DYNAMIC FUZZY MEASURE FOR MULTIPLE CLASSIFIER FUSION

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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： YA-JING ZHANG XUE-FEI LI JUN-FEN CHEN HUI-MIN FENG College of Science Agricultural University of Hebei Baoding 071001 China Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie

It has been shown that the fuzzy integral is an effective tool for the fusion of multiple classifiers. Of primary importance in the development of the system is the choice of the measure which embodies the importance of subsets of classifiers. In this paper we propose a method for a dynamic fuzzy measure which will change following the pattern to be classified (data dependent). This method uses the neural network which has good study ability. Our experiment results show that this method make the classification accurate improve.

关键词： Multiple classifier Fusion Fuzzy measure Fuzzy integral Neural network

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Refining Width Parameter of Gaussian Kernel Functionfor Tuning Coherent Point Drift(CPD) Registration

Refining Width Parameter of Gaussian Kernel Functionfor Tuni...

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作者： Junfen CHEN Qiang HE Yan LI Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Information Sciences Hebei University School of Sciences Beijing University of Civil Engineering and Architecture

Coherent point drift(CPD), a sophistic non-rigid point sets registration method, is successfully applied in computer vision, medical image analysis, to name a few. Its registration error, however, is affected greatly by three free parameters. One of them, width parameter of Gaussian kernel function, is studied in this paper to tune registration error of the CPD method. Before computing width parameter by a heuristic algorithm, the given data is regulated using minmax and standard normalization in effort to remove heterogeneity among the features of the data. Several experiments are designed on the availab.e six datasets to examine the effectiveness of CPD based on the refined width parameter. Experimental comparison indicated that the refined width parameter from the normalized data can reduce registration error of the CPD method, e.g., by 13.76% on bat dataset.

关键词： Normalization Gaussian kernel Width parameter Coherent point drift(CPD) Registration error(MSE)

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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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Core Set Extreme learning machine

Core Set Extreme Learning Machine

引用

作者： Shuxia Lu Bin Liu Caihong Jiao Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University School of Mechanical and Vehicular Engineering Beijing Institute of Technology

A core set extreme learning machine(CSELM) approach is proposed in order to deal with large datasets classification problem. In the first stage, the core set can be obtained efficiently by using the generalized core vector machine(GCVM) algorithm. For the second stage, the extreme learning machine(ELM) can be used to implement classification for much larger datasets. Experiments show that the CSELM has comparable performance with SVM and ELM implementations, but is faster on large datasets.

关键词： Core vector machine Extreme learning machine Support vector machine Core set

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