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Feature selection has become the focus of many real-world application oriented developments and applied research in recent years. With the rapid advancement of computer and database technologies, problems "with hundreds and thousands of variables or features are now ubiquitous in pattern recognition, data mining, and machine learning [1], [2]. In this article, we consider two real-world feature...
In this paper, a new algorithm related with feature selection method mostly used in data mining, machine learning and pattern recognition areas is proposed. Classical Fukunaga-Koontz Transform is extended to a binary kernel classifier. We used cDNA microarrays to assess 11.000 gene expression profiles in 60 human cancer cell lines used in a drug discovery screen by the National Cancer Institute and...
When solving the problem in computer assisted detection by the approach of pattern recognition, the lesion data always exhibited high-dimensional and inhomogeneous, which makes most of the traditional classifiers can not performance very well. In this paper, a novel approach based on the dynamic feature subset selection and the EM algorithm with Naive Bayesian classifier integration algorithm (DSFS+EMNB)...
In the paper we present a brief survey of our results in processing of data from DNA microarray experiments obtained in our collaborative research with M.C. Sklodowska Centre of Oncology. Our experience therefore is strictly connected with problems resulting from cancer diagnosis and therapy but many results have more general issue. We focus our attention on three important stages of microarray data...
Fuzzy rule base classification systems have been the focus of increased attention in recent years, due to their unique capability of providing human experts with outcomes by means of linguistic rules. In the same time period classifier fusion approaches have been shown to enhance the performance of pattern recognition systems. In the present study we applied a hybrid random subspace fusion scheme...
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