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A Study of Instance Selection Algorithm Based on Euclidean Distance

Author: HanGuangHui
Tutor: WangXiZhao
School: Hebei University
Course: Applied Mathematics
Keywords: Instance selection
CLC: TP301.6
Type: Master's thesis
Year: 2011
Downloads: 36
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Abstract


Neighbor classification in training classifier need storage training focus all of the data, this kind of defects can lead to program at run time need a lot of storage space and running time. Accordingly, this paper puts forward two new Instance Selection algorithm: Iterative Class Instance Selection algorithm ICIS (Iterative Class Instance Selection) and based on the similar and Different Iterative Class Instance Selection algorithm IISDC (Iterator Instance Selection based on Same and Different Classes). Two algorithms are proposed classification ability evaluation function to measure each instance classification ability, select the classification ability of the instance, delete the classification ability weak examples. The analysis in this paper two algorithm of time complexity are O (n ~ 2). In the real database on the experimental results show that ICIS and IISDC algorithm in compression ratio, the classification accuracy is better than that of FCNN, ICF, ENN classic algorithm.

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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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