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Research of Multiattribute and Large-scale Data Classification Algorithm Based on Support Vector Machine

Author: HouTieMin
Tutor: ChenXueGuang；LiuZhenYuan
School: Huazhong University of Science and Technology
Course: Systems Engineering
Keywords: Data Mining Support Vector Machine Training algorithm Sequential Minimal Optimization Attribute reduction
CLC: TP301.6
Type: Master's thesis
Year: 2007
Downloads: 147
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Abstract


Data mining is the process from large, complex data quickly obtain a novel, effective knowledge . Classification predict unknown data by a classifier trained by empirical data attribution , is one of the most common data mining tasks . Support vector machines , because of its excellent learning performance has become a research hotspot of machine learning community , and have made successful applications in many fields . However , as an emerging technology in the field of data mining classification , support vector machines have yet to be explored and perfected . This paper introduced the basic theory of support vector machine and its training algorithm based on focused sequential minimal optimization (Sequential Minimal Optimization, SMO) algorithm . The SMO algorithm effective algorithm for large-scale training data set , but there are still slow training speed , the big drawback of space . This paper presents a double SMO algorithm . The approximate classification hyperplane algorithm using SMO algorithm on the sampling data set of the original data set , again using SMO algorithm based on support vector approximation hyperplanes of the original data set , the final classification hyperplane . Double SMO algorithm reduces the space, and to some extent to eliminate the noise point the final classification hyperplane impact accelerate the optimization process . Since data mining often have to deal with large - scale multi-attribute data set , so you need to carry out the the attributes reduction treatment to reduce the amount of calculation and improve the algorithm speed , and mining classification model is easy to double SMO algorithm understand. Based on this, the issue of multi- attribute data mining made ??the discussion of attribute reduction , attribute reduction double SMO algorithm . The algorithm is applicable to the classification problem in data mining provides a theoretical basis for the establishment of a data mining program . To verify the validity of the double SMO algorithm uses the algorithm to test the two-dimensional data sets , and attributes about less double SMO algorithm to establish a data mining program . The results show that the algorithm improves the the SMO algorithm 's performance , shorten the training time , reducing the space , and the accuracy rate of better than decision trees, neural networks and Bayesian algorithms . Support vector machines introduced data mining, data mining system designed to provide a new choice .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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