Open Access Open Access  Restricted Access Subscription or Fee Access

K-Nearest Neighbour and Support Vector Machine Hybrid Classification

A. M. Hafiz

Abstract



In this paper, a novel K-Nearest Neighbour and Support Vector Machine hybrid classification technique has been proposed that is simple and robust. It is based on the concept of discriminative nearest neighbourhood classification. The technique consists of using K-Nearest Neighbour Classification for test samples satisfying a proximity condition. The patterns which do not pass the proximity condition are separated. This is followed by sifting the training set for a fixed number of patterns for every class which are closest to each separated test pattern respectively, based on the Euclidean distance metric. Subsequently, for every separated test sample, a Support Vector Machine is trained on the sifted training set patterns associated with it, and classification for the test sample is done. The proposed technique has been compared to the state of art in this research area. Three datasets viz. the United States Postal Service (USPS) Handwritten Digit Dataset, MNIST Dataset, and an Arabic numeral dataset – the Modified Arabic Digits Database (MADB), have been used to evaluate the performance of the algorithm. The algorithm generally outperforms the other algorithms with which it has been compared.

Keywords


hybrid classifier; MNIST; machine learning; artificial intelligence;

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.