A. Kothari, A. Keskar, Allhad Gokhale, Rucha Deshpande, Pranjali P. Deshmukh
{"title":"Rough Set Approach for Feature Reduction in Pattern Recognition through Unsupervised Artificial Neural Network","authors":"A. Kothari, A. Keskar, Allhad Gokhale, Rucha Deshpande, Pranjali P. Deshmukh","doi":"10.1109/ICETET.2008.230","DOIUrl":null,"url":null,"abstract":"The rough set approach can be applied in pattern recognition at three different stages: pre-processing stage, training stage and in the architecture. This paper proposes the application of the Rough-Neuro Hybrid Approach in the pre-processing stage of pattern recognition. In this project, a training algorithm has been first developed based on Kohonen network. This is used as a benchmark to compare the results of the pure neural approach with the Rough-Neuro hybrid approach and to prove that the efficiency of the latter is higher. Structural and statistical features have been extracted from the images for the training process. The number of attributes is reduced by calculating reducts and core from the original attribute set, which results into reduction in convergence time. Also, the above removal in redundancy increases speed of the process reduces hardware complexity and thus enhances the overall efficiency of the pattern recognition algorithm.","PeriodicalId":269929,"journal":{"name":"2008 First International Conference on Emerging Trends in Engineering and Technology","volume":"59 34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 First International Conference on Emerging Trends in Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETET.2008.230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The rough set approach can be applied in pattern recognition at three different stages: pre-processing stage, training stage and in the architecture. This paper proposes the application of the Rough-Neuro Hybrid Approach in the pre-processing stage of pattern recognition. In this project, a training algorithm has been first developed based on Kohonen network. This is used as a benchmark to compare the results of the pure neural approach with the Rough-Neuro hybrid approach and to prove that the efficiency of the latter is higher. Structural and statistical features have been extracted from the images for the training process. The number of attributes is reduced by calculating reducts and core from the original attribute set, which results into reduction in convergence time. Also, the above removal in redundancy increases speed of the process reduces hardware complexity and thus enhances the overall efficiency of the pattern recognition algorithm.