Classification of multi class dataset using wavelet power spectrum

• Autores: S. Prabakaran, Rajendra Sahu, Sekher Verma
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 15, Nº 3, 2007, págs. 297-319
• Idioma: inglés
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• Resumen

  • Data mining techniques are widely used in many fields. One of the applications of data mining in the field of the Bioinformatics is classification of tissue samples. In the present work, a wavelet power spectrum based approach has been presented for feature selection and successful classification of the multi class dataset. The proposed method was applied on SRBCT and the breast cancer datasets which are multi class cancer datasets. The selected features are almost those selected in previous works. The method was able to produce almost 100% accurate classification results. The method is very simple and robust to noise. No extensive preprocessing is required. The classification was performed with comparatively very lesser number of features than those used in the original works. No information is lost due to the initial pruning of the data usually performed using a threshold in other methods. The method utilizes the inherent nature of the data in performing various tasks. So, the method can be used for a wide range of data.