Accelerating Kernel Classifiers Through Borders Mapping

TL;DR

This paper introduces a method to convert kernel classifiers like SVMs into faster, piecewise linear classifiers by mapping decision borders, significantly improving classification speed on various datasets.

Contribution

A novel approach to derive piecewise linear classifiers from kernel classifiers, enhancing speed while maintaining accuracy, especially for problems with smooth probability functions.

Findings

Improved classification speed by up to two orders-of-magnitude on 12 datasets.

Method is fast to train due to individual component construction.

Effective mainly for problems with continuum features and smooth probability functions.

Abstract

Support vector machines (SVM) and other kernel techniques represent a family of powerful statistical classification methods with high accuracy and broad applicability. Because they use all or a significant portion of the training data, however, they can be slow, especially for large problems. Piecewise linear classifiers are similarly versatile, yet have the additional advantages of simplicity, ease of interpretation and, if the number of component linear classifiers is not too large, speed. Here we show how a simple, piecewise linear classifier can be trained from a kernel-based classifier in order to improve the classification speed. The method works by finding the root of the difference in conditional probabilities between pairs of opposite classes to build up a representation of the decision boundary. When tested on 17 different datasets, it succeeded in improving the classification speed of a SVM for 12 of them by up to two orders-of-magnitude. Of these, two were less accurate than a simple, linear classifier. The method is best suited to problems with continuum features data and smooth probability functions. Because the component linear classifiers are built up individually from an existing classifier, rather than through a simultaneous optimization procedure, the classifier is also fast to train.