Efficient Learning of Pinball TWSVM using Privileged Information and its applications

• URL: http://arxiv.org/abs/2107.06744v1
• Date: Wed, 14 Jul 2021 14:42:07 GMT
• Title: Efficient Learning of Pinball TWSVM using Privileged Information and its applications
• Authors: Reshma Rastogi (nee. Khemchandani) and Aman Pal
• Abstract summary: We propose privileged information based Twin Pinball Support Vector Machine classifier (Pin-TWSVMPI) The proposed Pin-TWSVMPI incorporates privileged information by using correcting function so as to obtain two nonparallel decision hyperplanes. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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