AVNM: A Voting based Novel Mathematical Rule for Image Classification. (December 2016)

• Record Type: Journal Article
• Authors: Vidyarthi, Ankit; Mittal, Namita
• Is Part Of: Computer methods and programs in biomedicine. Volume 137(2016)
• Page Start: 195
• Page End: 201
• Publication Date: 2016-12
• Subjects: Machine learning -- Classification -- K-Nearest Neighbor; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.08.015
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: AVNM automates the selection of the number of nearest neighbors for classification. AVNM is based on the concept of sub-space reduction based on features. Experiments are performed over UCI datasets and medical dataset of validation. Outperforms in respect of accuracy with state-of-art KNN algorithm and its variants. Abstract: Background and objectives: In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of a number of nearest neighbors i.e. "k" for computation. At present, it is hard to find the optimal value of "k" using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate. Method: Motivated by the prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. The proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. The proposed classifier also reduces the effect of outliers. Results: To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard datasets taken from UCI database and one manually created dataset. The experimental result shows that the proposed AVNM rule outperforms the KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule. Conclusions: The proposed AVNM rule is based on sample space reduction mechanism for identification of an optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with the state-of-art algorithm, KNN, and its variants. The proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manually created dataset.