Efficient Learning of Pinball TWSVM using Privileged Information and its applications

TL;DR

This paper introduces a novel privileged information-based Twin Pinball Support Vector Machine (Pin-TWSVMPI) that leverages expert knowledge to improve classification accuracy and efficiency, especially in noisy environments and real-world applications.

Contribution

It proposes a new Pin-TWSVM model incorporating privileged information with a correcting function and uses SMO for faster computation, applied to practical tasks like pedestrian detection and digit recognition.

Findings

Enhanced classification accuracy on UCI datasets.

Reduced computational time compared to traditional methods.

Effective in noisy environments and real-world applications.

Abstract

In any learning framework, an expert knowledge always plays a crucial role. But, in the field of machine learning, the knowledge offered by an expert is rarely used. Moreover, machine learning algorithms (SVM based) generally use hinge loss function which is sensitive towards the noise. Thus, in order to get the advantage from an expert knowledge and to reduce the sensitivity towards the noise, in this paper, we propose privileged information based Twin Pinball Support Vector Machine classifier (Pin-TWSVMPI) where expert's knowledge is in the form of privileged information. The proposed Pin-TWSVMPI incorporates privileged information by using correcting function so as to obtain two nonparallel decision hyperplanes. Further, in order to make computations more efficient and fast, we use Sequential Minimal Optimization (SMO) technique for obtaining the classifier and have also shown its application for Pedestrian detection and Handwritten digit recognition. Further, for UCI datasets, we first implement a procedure which extracts privileged information from the features of the dataset which are then further utilized by Pin-TWSVMPI that leads to enhancement in classification accuracy with comparatively lesser computational time.