A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement

TL;DR

This paper introduces ARES, a new data pre-processing method based on ensemble ranks over sub-samples, which enhances robustness to units and scales in data mining tasks like classification and anomaly detection.

Contribution

The paper proposes ARES, an innovative pre-processing technique that improves data normalization by combining ranks over multiple sub-samples, outperforming traditional methods in consistency and effectiveness.

Findings

ARES yields more consistent outcomes across algorithms and datasets.

ARES outperforms min-max normalization and traditional rank transformation.

ARES provides better or comparable results in classification and anomaly detection.

Abstract

Many existing data mining algorithms use feature values directly in their model, making them sensitive to units/scales used to measure/represent data. Pre-processing of data based on rank transformation has been suggested as a potential solution to overcome this issue. However, the resulting data after pre-processing with rank transformation is uniformly distributed, which may not be very useful in many data mining applications. In this paper, we present a better and effective alternative based on ranks over multiple sub-samples of data. We call the proposed pre-processing technique as ARES | Average Rank over an Ensemble of Sub-samples. Our empirical results of widely used data mining algorithms for classification and anomaly detection in a wide range of data sets suggest that ARES results in more consistent task specific? outcome across various algorithms and data sets. In addition to this, it results in better or competitive outcome most of the time compared to the most widely used min-max normalisation and the traditional rank transformation.