Mahalanobis Distance Metric Learning Algorithm for Instance-based Data Stream Classification

TL;DR

This paper introduces an online Mahalanobis distance metric learning algorithm for instance-based data stream classification, improving adaptability and accuracy in dynamic environments with concept drift.

Contribution

It presents a novel online Mahalanobis metric learning approach integrated with k-NN and concept drift detection for data streams.

Findings

Outperforms existing instance-based data stream classifiers

Effectively adapts to concept drift in various datasets

Demonstrates improved classification accuracy

Abstract

With the massive data challenges nowadays and the rapid growing of technology, stream mining has recently received considerable attention. To address the large number of scenarios in which this phenomenon manifests itself suitable tools are required in various research fields. Instance-based data stream algorithms generally employ the Euclidean distance for the classification task underlying this problem. A novel way to look into this issue is to take advantage of a more flexible metric due to the increased requirements imposed by the data stream scenario. In this paper we present a new algorithm that learns a Mahalanobis metric using similarity and dissimilarity constraints in an online manner. This approach hybridizes a Mahalanobis distance metric learning algorithm and a k-NN data stream classification algorithm with concept drift detection. First, some basic aspects of Mahalanobis distance metric learning are described taking into account key properties as well as online distance metric learning algorithms. Second, we implement specific evaluation methodologies and comparative metrics such as Q statistic for data stream classification algorithms. Finally, our algorithm is evaluated on different datasets by comparing its results with one of the best instance-based data stream classification algorithm of the state of the art. The results demonstrate that our proposal is better