Dynamic Feature Scaling for Online Learning of Binary Classifiers
This addresses a practical issue for online learning systems by enabling adaptive feature scaling, though it is incremental as it builds on existing scaling techniques.
The paper tackles the problem of feature scaling in online learning, where traditional preprocessing fails due to limited initial data and changing distributions, by proposing a dynamic scaling method that adapts during training. The method consistently outperforms more complex scaling approaches on benchmark datasets and improves the accuracy of a state-of-the-art online binary classifier.
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.