title: A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis.
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.
authors.affiliation: Department of Computer Science and Engineering, RMK Engineering College, Anna University, Chennai, India.
authors.forename: Jaison
authors.initials: J
authors.lastname: Bennet
authors.affiliation: Department of Computer Science and Engineering, RMK Engineering College, Anna University, Chennai, India.
authors.forename: Chilambuchelvan Arul
authors.initials: CA
authors.lastname: Ganaprakasam
authors.affiliation: Department of Information Science and Technology, Anna University, Chennai, India.
authors.forename: Kannan
authors.initials: K
authors.lastname: Arputharaj
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine; D058067:Wavelet Analysis