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Discretization of continuous-valued attributes in decision tree generation

Discretization of continuous-valued attributes in decision t...

引用

International Conference on machine learning and Cybernetics

作者： Li, Wen-Liagn Yu, Rui-Hua Wang, Xi-Zhao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China Department of Information Technology Baoding University Baoding 071000 China

ISBN: (纸本)9781424465262

Decision tree is one of the most popular and widely used classification models in machine learning. The discretization of continuous-valued attributes plays an important role in decision tree generation. In this paper, we improve Fayyad's discretization method which uses the average class entropy of candidate partitions to select boundaries for discretization. Our method can reduce the number of candidate boundaries further. Here we also propose a generalized splitting criterion for cut point selection and prove that the cut points are always on boundaries when using this criterion. Along with the formal proof, we present empirical results that the decision trees generated by using such criteria are similar on several datasets from the UCI machine learning Repository. © 2010 IEEE.

关键词： Decision trees

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CET4 passing rate analysis based on fuzzy decision tree induction and active learning

CET4 passing rate analysis based on fuzzy decision tree indu...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Qiao, Qing-Shui Wang, Hai-Tao Wang, Zhen-Yu Zhai, Jun-Hai Dept. of English Hebei Institute of Civil Engineering and Architecture Zhangjiakuo City Hebei China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding City China

ISBN: (纸本)9781457703065

College English Test Band Four (CET4) in China has been a significant impact on evaluating the English preliminary level of a college student or a class. How to improve the college English teaching and go further to raise passing rate of CET4 are a challenge for many colleges and universities. This paper makes an attempt to quantitatively analyze the CET4 and exam-related factors by using fussy decision tree technique and active learning based on uncertainty. Several features are selected to formulate this problem. The weighted margin is proposed as the new uncertainty measure criterion for unlab.led instance, and a density measure is introduced for avoiding selecting isolated instances. Experiments and simulations on different classes of students show the proposed quantitative analysis method is feasible and effective, which can provide teachers with some useful guidelines for how to improve the college English teaching. © 2011 IEEE.

关键词： Decision trees

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Demonstrating principal component aggregation for distributed spatial pattern recognition

Demonstrating principal component aggregation for distribute...

引用

9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010

作者： Le Borgne, Yann-Aël Nowé, Ann Steenhaut, Kris Bontempi, Gianluca Computational Modeling Lab. Vrije Universiteit Brussel Brussels Belgium Etro Lab. Vrije Universiteit Brussel Brussels Belgium Machine Learning Group Université Libre de Bruxelles Brussels Belgium

ISBN: (纸本)9781605589886

The Principal Component Aggregation has recently been proposed as a versatile distributed information extraction technique for sensor networks [3]. This demonstration illustrates its use for a network-level pattern recognition task. Four different patterns, or events, may be sensed by light measurements of a network of 27 nodes. The sens measurements are fused on the fly along a routing tree up to the base station, where the monitored pattern is recognized by a prediction algorithm.

关键词： Trees (mathematics)

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Optimization methods for resources allocation in real-time strategy games

Optimization methods for resources allocation in real-time s...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Tong, Xiao-Lei Li, Yan Li, Wen-Liang Zhang, Lei Key Lab. In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China Handan Hospital of Traditional Chinese Medicine Handan 056000 China

ISBN: (纸本)9781457703065

In order to meet the demands of the real time strategy (RTS) games, two learning methods are proposed based on genetic algorithm (GA) and Particle swarm optimization (PSO) to handle the problem of multi-team weapon target assignment (MT-WTA) and distribution of defensive position with restrictive limit of weapon resource. The goal is to take the greatest destruction on the targets. Firstly, we use GA to assign different types of weapons under limited resources. Secondly, we put the optimal results from the first stage into a random game's map to obtain final defensive locations. Then GA and PSO are used to achieve the best distribution of defensive positions respectively and their performance is compared in RTS Games. Both of these two methods have provided efficient, interesting AI to solve real-time strategy games problems, the experimental results can well support this point. © 2011 IEEE.

关键词： Genetic algorithms

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Potential support vector machine based on the reduced samples

Potential support vector machine based on the reduced sample...

引用

International Conference on Information Science and Engineering (ICISE)

作者： Shu-xia Lu Gui-en Cao Jie Meng Hua-chao Wang Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding Hebei China

When the training dataset is very large, the learning process of potential support vector machine takes up so large memory that the training speed is very slow. To accelerate the training speed of the potential support vector machine (PSVM) for large-scale datasets, a new method is proposed, which introduces PSVM based on the reduced samples. The new method removes most non-support vectors, and keeps the samples on and near the boundary, which may be the support vectors, as the new training samples. This method is more suitable to large-scale datasets. The experimental results show that the proposed method performs well to decrease the consumption of computer memory, and accelerate the training speed of PSVM.

关键词： Support vector machines Classification algorithms Training Algorithm design and analysis Optimization Accuracy Acceleration

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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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A Total Error Rate Multi-class Classification

A Total Error Rate Multi-class Classification

引用

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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SGM: Sequence generation model for multi-lab.l classification 27

SGM: Sequence generation model for multi-label classificatio...

引用

27th International Conference on computational Linguistics, COLING 2018

作者： Yang, Pengcheng Sun, Xu Li, Wei Ma, Shuming Wu, Wei Wang, Houfeng Deep Learning Lab. Beijing Institute of Big Data Research Peking University China MOE Key Lab of Computational Linguistics School of EECS Peking University China

ISBN: (纸本)9781948087506

Multi-lab.l classification is an important yet challenging task in natural language processing. It is more complex than single-lab.l classification in that the lab.ls tend to be correlated. Existing methods tend to ignore the correlations between lab.ls. Besides, different parts of the text can contribute differently to predicting different lab.ls, which is not considered by existing models. In this paper, we propose to view the multi-lab.l classification task as a sequence generation problem, and apply a sequence generation model with a novel decoder structure to solve it. Extensive experimental results show that our proposed methods outperform previous work by a substantial margin. Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between lab.ls, but also select the most informative words automatically when predicting different lab.ls. © 2018 COLING 2018 - 27th International Conference on computational Linguistics, Proceedings. All rights reserved.

关键词： Classification (of information)

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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 scalab.lity 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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