A Weighted Mutual k-Nearest Neighbour for Classification Mining

TL;DR

This paper introduces a weighted mutual k-nearest neighbor algorithm that enhances classification accuracy by detecting anomalies, removing pseudo neighbors, and weighting closer neighbors, especially effective in noisy, large-scale datasets.

Contribution

It proposes a novel learning algorithm combining anomaly detection, pseudo neighbor removal, and weighted voting to improve kNN classification in noisy data environments.

Findings

Improved classification accuracy on large-scale noisy datasets

Effective anomaly detection and pseudo neighbor removal

Enhanced neighbor weighting scheme improves results

Abstract

kNN is a very effective Instance based learning method, and it is easy to implement. Due to heterogeneous nature of data, noises from different possible sources are also widespread in nature especially in case of large-scale databases. For noise elimination and effect of pseudo neighbours, in this paper, we propose a new learning algorithm which performs the task of anomaly detection and removal of pseudo neighbours from the dataset so as to provide comparative better results. This algorithm also tries to minimize effect of those neighbours which are distant. A concept of certainty measure is also introduced for experimental results. The advantage of using concept of mutual neighbours and distance-weighted voting is that, dataset will be refined after removal of anomaly and weightage concept compels to take into account more consideration of those neighbours, which are closer. Consequently, finally the performance of proposed algorithm is calculated.