DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING
Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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ICT Academy of Tamil Nadu
2012-04-01
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| Series: | ICTACT Journal on Soft Computing |
| Subjects: | |
| Online Access: | http://ictactjournals.in/paper/IJSC_Vol2_Iss3_7_Paper_348_352.pdf |
| _version_ | 1811281808972578816 |
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| author | M. Safish Mary V. Joseph Raj |
| author_facet | M. Safish Mary V. Joseph Raj |
| author_sort | M. Safish Mary |
| collection | DOAJ |
| description | Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making. |
| first_indexed | 2024-04-13T01:40:16Z |
| format | Article |
| id | doaj.art-e7fa1cde4b0e4e6aa7cc30ab95b74c92 |
| institution | Directory Open Access Journal |
| issn | 0976-6561 2229-6956 |
| language | English |
| last_indexed | 2024-04-13T01:40:16Z |
| publishDate | 2012-04-01 |
| publisher | ICT Academy of Tamil Nadu |
| record_format | Article |
| series | ICTACT Journal on Soft Computing |
| spelling | doaj.art-e7fa1cde4b0e4e6aa7cc30ab95b74c922022-12-22T03:08:13ZengICT Academy of Tamil NaduICTACT Journal on Soft Computing0976-65612229-69562012-04-0123348352DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERINGM. Safish Mary0V. Joseph Raj1Department of Computer Science, St. Xavier’s College (Autonomous), IndiaDepartment of Computer Science, Kamaraj College, IndiaClassification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.http://ictactjournals.in/paper/IJSC_Vol2_Iss3_7_Paper_348_352.pdfRadial Basis Function Neural NetworkGradient DescentSpherical Gaussian FunctionFeature ExtractionInstance-based Data Selection |
| spellingShingle | M. Safish Mary V. Joseph Raj DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING ICTACT Journal on Soft Computing Radial Basis Function Neural Network Gradient Descent Spherical Gaussian Function Feature Extraction Instance-based Data Selection |
| title | DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING |
| title_full | DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING |
| title_fullStr | DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING |
| title_full_unstemmed | DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING |
| title_short | DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING |
| title_sort | data classification with neural classifier using radial basis function with data reduction using hierarchical clustering |
| topic | Radial Basis Function Neural Network Gradient Descent Spherical Gaussian Function Feature Extraction Instance-based Data Selection |
| url | http://ictactjournals.in/paper/IJSC_Vol2_Iss3_7_Paper_348_352.pdf |
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