Abstract—The datasets used in many real applications are highly imbalanced which makes classification problem hard. Classifying the minor class instances is difficult due to bias of the classifier output to the major classes. Nearest neighbor is one of the most popular and simplest classifiers with good performance on many datasets. However, correctly classifying the minor class is commonly sacrificed to achieve a better performance on others. This paper is aimed to improve the performance of nearest neighbor in imbalanced domains, without disrupting the real data distribution. Prototype-weighting is proposed, here, to locally adapting the distances to increase the chance of prototypes from minor class to be the nearest neighbor of a query instance. The objective function is, here, G-mean and optimization process is performed using gradient ascent method. Comparing the experimental results, our proposed method significantly outperformed similar works on 24 standard data sets.

Index Terms—Gradient ascent, imbalanced data, nearest neighbor, weighted distance.

The authors are with the Department of Computer Science and Engineering and Information Technology, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran (email: z-hajizadeh, taheri@cse.shirazu.ac.ir, zjahromi@shirazu.ac.ir).

Cite:Zahra Hajizadeh, Mohammad Taheri, and Mansoor Zolghadri Jahromi, "Nearest Neighbor Classification with Locally Weighted Distance for Imbalanced Data," International Journal of Computer and Communication Engineering vol. 3, no. 2, pp. 81-86, 2014.