期刊名称：International Journal of Advanced Computer Research
印刷版ISSN：2249-7277
电子版ISSN：2277-7970
出版年度：2013
卷号：3
期号：10
页码：200-204
出版社：Association of Computer Communication Education for National Triumph (ACCENT)
摘要：Unclassified region deceases the efficiency and performance of PLSA and FLDA. The proper selection of feature sub set reduced the unclassified region of PLSA and FLDA. Now a day’s binary classification are widely used in image classification. The mapping of data one space to another space creates diversity of outlier and noise and generate unclassified region for image classification. For the reduction of unclassified region we used radial basis function for sampling of feature and reduce the noise and outlier for feature space of data and increase the performance and efficiency of image classification. Our proposed method optimized the feature selection process and finally sends data to FLDA classifier for classification of data. Here we used fisher classifier. As a classifier FLDA suffering two problems (1) how to choose optimal feature sub set input and (2) how to set best kernel parameters. These problems influence the performance and accuracy of FLDA. Now the pre-sampling of feature reduced the feature selection process of FLDA for image classification.
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