Multi-level classification: A generic classification method for medical datasets

Srinivas, M and R, Bharath and P, Rajalakshmi and C, Krishna Mohan (2015) Multi-level classification: A generic classification method for medical datasets. In: 17th International Conference on E-health Networking, Application & Services (HealthCom), 14-17, Oct. 2015, Boston, MA.

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Classification of medical data is one of the most challenging pattern recognition problems. As stated in literature a single classifier is unable to solve all medical image classification problems due to high sensitivity to noise and other imperfections like data imbalance. So, several individual classifiers have been studied to solve the different types of classification problems arising in medical datasets but all have proven to be useful on some specific datasets. Hence, in this paper, we propose a generic multi-level classification approach for medical datasets using sparsity based dictionary learning and support vector machine approaches. The proposed technique demonstrates the following advantages: 1) gives better performance of classification accuracy over all datasets 2) solves imbalanced data problems 3) needs no fusion and ensemble methods in multi-level classification. The results presented on the 5 standard UCI medical datasets demonstrate that the efficacy of the proposed multi-level classification technique.

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Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Multi-level classification, Multiple classifiers, Single classifier, Fusion, On-line dictionary learning, Sparse representation, Support vector machines, Breast mass, Heart dataset, SPECTF dataset
Subjects: Others > Electricity
Divisions: Department of Electrical Engineering
Depositing User: Library Staff
Date Deposited: 27 May 2016 06:45
Last Modified: 01 Sep 2017 09:19
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