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Evolutionary Computation in the medical application of data mining

Author: LiuQiuYun
Tutor: WangHanHu
School: Guizhou University
Course: Applied Computer Technology
Keywords: Evolutionary Computation Medical Data Mining Gene Expression Programming Attribute Reduction Rough Set Nuclear Methods Microarray data
CLC: TP311.13
Type: Master's thesis
Year: 2009
Downloads: 49
Quote: 0
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Abstract


Any one discipline are inseparable from the rapid development of supporting and promoting social demand , as people 's quality of life and health of the growing importance of this century, medicine has become the fastest growing disciplines. In the process of rapid development of medical science has accumulated a large amount of data , no doubt, most of which contain medical data are extremely valuable information, so how to effectively manage and use these data to become a serious problem , which gave birth to the medical information science and bioinformatics derivative which two disciplines, their focus, research methods, objects , etc. There are many differences , but for medical data analysis and data mining techniques is that the two disciplines , but a common focus . In this paper, in-depth study of gene expression programming and data mining technology, based on the two proposed for medical data mining algorithms: based on gene expression programming and rough sets attribute reduction algorithm based on gene expression programming GEPFS and the kernel K neighbor classifier GEPKNN. GEPFS for discrete data attribute reduction , it aims at minimizing the subset of attributes and maximizing classification accuracy balance between the two sides , experiments show that the quality of data after reduction did get a certain degree of improvement . GEPKNN is trying to improve the current widely used in the field of bioinformatics nuclear K-nearest neighbor classifier, it automatically K-nearest neighbor classifier for the nuclear structure and data-related kernel function to avoid the artificial designation subjective and arbitrary kernel function , thereby enhancing the nuclear K-nearest neighbor classifier performance. In the last article , we put these two algorithms are of practical value for the two medical data mining problem - automated guiding patients and microarray data classification . Experiments show that in their respective fields of application have achieved relatively good results

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