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Uncertain decision tree for bank marketing classification

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2020年第0期371卷 112710-000页

作者： Yang, Shiueng-Bien Chen, Tai-Liang Wenzao Ursuline Univ Languages Dept Digital Content Applicat & Management 900 Mintsu 1st Rd Kaohsiung 80424 Taiwan

This study proposes a novel decision tree for uncertain data, called the uncertain decision tree (UDT), based on the uncertain genetic clustering algorithm (UGCA). UDT extends the decision tree to handle data with uncertain information, in which the uncertainty must be considered to obtain high quality results. In UDT, UGCA automatically searches for the proper number of branches of each node, based on the classification error rate and the classification time of UDT. Restated, UGCA reduces both the classification error rate and computing time and, then, optimizes the proposed UDT. Before the UDT is designed using UGCA, an uncertain merging algorithm (UMA) is also developed to reduce the uncertain data set, thereby allowing UGCA to process a large data set efficiently. Importantly, experimental results demonstrate that the proposed UDT outperforms traditional uncertain decision trees. (C) 2020 Published by Elsevier B.V.

关键词： Decision trees genetic clustering algorithm Uncertain data Merging algorithm

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Quantum-Inspired Immune Clonal clustering algorithm Based on Watershed

Quantum-Inspired Immune Clonal Clustering Algorithm Based on...

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2010 IEEE World Congress on Computational Intelligence

作者： Li, Yangyang Wu, Nana Ma, Jingjing Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9781424481262

Based on the concepts and principles of quantum computing, a novel clustering algorithm, called a quantum-inspired immune clonal clustering algorithm based on watershed (QICW), is proposed to deal with the problem of image segmentation. In QICW, antibody is proliferated and divided into a set of subpopulation groups. Antibodies in a subpopulation group are represented by multi-state gene quantum bits. In the antibody's updating, the quantum mutation operator is applied to accelerate convergence. The quantum recombination realizes the information communication between the subpopulation groups so as to avoid premature convergences. In this paper, the segmentation problem is viewed as a combinatorial optimization problem, the original image is partitioned into small blocks by watershed algorithm, and the quantum-inspired immune clonal algorithm is used to search the optimal clustering centre, and make the sequence of maximum affinity function as clustering result, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with the genetic clustering algorithm based on watershed (W-GAC), and the k-means algorithm based on watershed (W-KM).

关键词： SAR image segmentation combinatorial mathematics combinatorial optimization problem genetic algorithms genetic clustering algorithm image segmentation image texture k-means algorithm maximum affinity function multistate gene quantum bits pattern clustering quantum computing quantum computing quantum mutation operator quantum-inspired immune clonal clustering algorithm synthetic aperture radar texture image watershed algorithm

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AN ACCESSORY clustering METHOD FOR GROUP LAYOUT

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38th International Conference on Computers and Industrial Engineering

作者： Chang, Jian-e Wang, Deshuang Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

ISBN: (纸本)9787121074370

Nowadays Group Layout (GL) becomes more popular as its flexibility and agility, the machine-part cell formation problem consists of constructing a set of machine cells and their corresponding processes with the objective of minimizing the inner-cell & external-cell accessory movement. This paper puts forward some application problems using Group Technology in GL and presents a genetic clustering algorithm combined with local optimization search arithmetic, establishes systemic process, realizes it based on Visual C++6.0. When tested by classical machine-part cell problems, the improved algorithm performs better than other methods on clustering adaptability, time, facility integrated using rate. This paper could provide core method for computer aided grouping layout and has impressive efforts on implying Lean Production & Manufacture Resource Planning for Medium-sized and small enterprises.

关键词： Group Layout Machine-part Cell Formation genetic clustering algorithm Local Optimization Search Arithmetic Lean Production

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Variable-branch tree-structured vector quantization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2004年第9期13卷 1275-1285页

作者： Yang, SB Leader Univ Dept Comp Sci & Informat Engn Tainan Taiwan

Tree-structured vector quantizers (TSVQ) and their variants have recently been proposed. All trees used are fixed M-ary tree structured, such that the training samples in each node must be artificially divided into a fixed number of clusters. This paper proposes a variable-branch tree-structured vector quantizer (VBTSVQ) based on a genetic algorithm, which searches for the number of child nodes of each splitting node for optimal coding in VBTSVQ. Moreover, one disadvantage of TSVQ is that the searched codeword usually differs from the full searched codeword. Briefly, the searched codeword in TSVQ sometimes is not the closest codeword to the input vector. This paper proposes the multiclassification encoding method to select many classified components to represent each cluster, and the codeword encoded in the VBTSVQ is usually the same as that of the full search. VBTSVQ outperforms other TSVQs in the experiments presented here.

关键词： genetic clustering algorithm multiclassification encoding method tree-structured vector quantizer (TSVQ)

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A genetic clustering algorithm for data with non-spherical-shape clusters

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PATTERN RECOGNITION 2000年第7期33卷 1251-1259页

作者： Tseng, LY Yang, SB Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan

In solving clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. The traditional neighborhood clustering algorithm usually needs the user to provide a distance d for the clustering. This d is difficult to decide because some clusters may be compact but others may be loose. In this paper, we propose a genetic clustering algorithm for clustering the data whose clusters are not of spherical shape. It can automatically cluster the data according to the similarities and automatically find the proper number of clusters. The experimental results are given to illustrate the effectiveness of the genetic algorithm. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： clustering genetic clustering algorithm non-spherical-shape clusters

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General-tree-structured vector quantizer for image progressive coding using the smooth side-match method

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2003年第2期13卷 193-202页

作者： Yang, SB Leader Univ Dept Comp Sci & Informat Engn Tainan Taiwan

Several tree-structured vector quantizers have recently been proposed. However, owing to the fact that all trees used are fixed M-ary tree-structured, the training samples contained,in each node must be artificially divided into a fixed number of clusters. This paper presents a general-tree-structured vector quantizer (GTSVQ) based on a genetic clustering algorithm that can divide the training samples contained in each node into more natural clusters. Also, the Huffman tree decoder is used to achieve the optimal bit rate after the construction of the general-tree-structured encoder. Progressive coding can be accomplished by giving a series of distortion or rate thresholds. Moreover, a smooth side-match method is presented herein to enhance the performance of coding quality according to the smoothness of the, gray levels be tween neighboring blocks. The combination of the Huffman tree decoder and the smooth side-match method is proposed herein. Furthermore, the Lena image can be coded by GTSVQ with 0.198 bpp and 34.3 dB in peak signal-to-noise ratio.

关键词： genetic clustering algorithm Huffman coding smooth side-match method tree-structured vector quantizer

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A genetic approach to the automatic clustering problem

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PATTERN RECOGNITION 2001年第2期34卷 415-424页

作者： Tseng, LY Yang, SB Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan

In solving the clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. Therefore, clustering becomes a tedious trial-and-error work and the clustering result is often not very promising especially when the number of clusters is large and not easy to guess. In this paper, we propose a genetic algorithm for the clustering problem. This algorithm is suitable for clustering the data with compact spherical clusters. It can be used in two ways. One is the user-controlled clustering, where the user may control the result of clustering by varying the values of the parameter, w. A small value of w results in a larger number of compact clusters, while a large value of w results in a smaller number of looser clusters. The other is an automatic clustering, where a heuristic strategy is applied to find a good clustering. Experimental results are given to illustrate the effectiveness of this genetic clustering algorithm. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： clustering single-linkage algorithm genetic clustering algorithm

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Smooth side-match classified vector quantizer with variable block size

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2001年第5期10卷 677-685页

作者： Yang, SB Tseng, LY Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan

Although the side-match vector quantizer (SMVQ) reduces the bit rate, the image coding quality by SMVQ generally degenerates as the gray level transition across the boundaries of the neighboring blocks is increasing or decreasing. This study presents a smooth side-match method to select a state codebook according to the smoothness of the gray levels between neighboring blocks. This method achieves a higher PSNR and better visual perception than SMVQ does for the same bit rate. Moreover, to design codebooks, a genetic clustering algorithm that automatically finds the appropriate number of clusters is proposed. The proposed smooth side-match classified vector quantizer (SSM-CVQ) is thus a combination of three techniques: the classified vector quantization, the variable block size segmentation and the smooth side-match method. Experiment al results indicate that SSM-CVQ has a higher PSNR and a lower bit rate than other methods. Furthermore, the Lena image can be coded by SSM-CVQ with 0.172 bpp and 32.49 dB in PSNR.

关键词： genetic clustering algorithm image coding smooth side-match classified vector quantizer

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