No abstract

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

The measurement of “chemical shift,” that is, the change in energy of an element's x-ray emission lines with the state of its chemical combination, has been carried out for some years. Of the three major aspects of the technique, two have received major attention. Nagel (1) has an excellent treatise on the interpretation of valence band x-ray spectra, while such workers as Fischer (2) and Koffman and Moll (3) have attempted to correlate the data with structure. The third area, convenient data collection, has not been so well investigated. Much, but not all, of the effort has been toward direct electron excitation with its attendant problems of sample damage due to high vacuum and electron bombardment effects.