Pattern-based Data-Classification Technique | ||||
| The International Conference on Electrical Engineering | ||||
| Article 95, Volume 7, 7th International Conference on Electrical Engineering ICEENG 2010, May 2010, Page 1-25 PDF (323.98 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2010.33265 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| M. Salama1; A. Hasanen2; A. Fahmy3 | ||||
| 1Egyptian Armed Forces. | ||||
| 2Benha High Technology Institute, Benha, Egypt. | ||||
| 3College of Engineering, Cairo University, Cairo, Egypt. | ||||
| Abstract | ||||
| Abstract: This paper presents a novel model of a supervised machine learning approach for classification of a dataset. The model depends on a feature selection (dimensionality reduction) method that is based on pattern-based subspace clustering technique. Then this clustering technique is applied to the dataset to perform the classification of the data. This approach is a non-statistical technique that supports most of the requirements that have been discussed recently like dimensionality reduction using multivariate feature selection method, threshold independence and handling of missing data. The approach tends to handle these requirements altogether which not the case in other classification models as discussed in this paper. Another distinguishing point in this model is its dependence on the variation of the values of relative features among different objects. Experimental results on synthetic and real datasets show that approach outperforms the existing methods in both efficiency and effectiveness. | ||||
| Keywords | ||||
| Feature Selection; classification; Patterns | ||||
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