Dynamic Feature Scaling for Online Learning of Binary Classifiers

TL;DR

This paper introduces a simple dynamic feature scaling method for online binary classifiers that adapts to changing data distributions, outperforming complex scaling methods in various benchmark tests.

Contribution

The paper presents a novel, effective dynamic feature scaling technique specifically designed for online learning scenarios, addressing limitations of pre-processing scaling.

Findings

Outperforms complex scaling methods on benchmark datasets

Improves accuracy of online binary classifiers

Adapts effectively to changing data distributions

Abstract

Scaling feature values is an important step in numerous machine learning tasks. Different features can have different value ranges and some form of a feature scaling is often required in order to learn an accurate classifier. However, feature scaling is conducted as a preprocessing task prior to learning. This is problematic in an online setting because of two reasons. First, it might not be possible to accurately determine the value range of a feature at the initial stages of learning when we have observed only a few number of training instances. Second, the distribution of data can change over the time, which render obsolete any feature scaling that we perform in a pre-processing step. We propose a simple but an effective method to dynamically scale features at train time, thereby quickly adapting to any changes in the data stream. We compare the proposed dynamic feature scaling method against more complex methods for estimating scaling parameters using several benchmark datasets for binary classification. Our proposed feature scaling method consistently outperforms more complex methods on all of the benchmark datasets and improves classification accuracy of a state-of-the-art online binary classifier algorithm.