A Parallelized Conjugate Gradient Algorithm for Monotonicity Constrained Support Vector Machines

Abstract

Data mining also known as knowledge discovery in database (KDD) is the computational process of discovering patterns from observed data for which classification is one of the most important tasks in data mining Many classification techniques including Support Vector Machines (SVMs) have been developed over the years Recently SVMs have become state-of-art classifiers due to their excellent ability in solving classification problems However SVMs also have drawbacks such as high computing cost with large amounts of data and high susceptibility to noisy data Various efforts have been made to improve SVMs based on different scenarios of real world problems Among them taking into account experts' knowledge has been confirmed to help SVMs deal with noisy data to gain more useful results For example SVMs with monotonicity constraints and with the Tikhonov regularization method also known as Regularized Monotonic SVM (RMC-SVM) incorporates inequality constraints into SVMs based on the monotonic property of real-world problems and use the Tikhonov regularization method is further applied to ensure that the solution is unique and bounded These kinds of SVMs are also referred to as knowledge-oriented SVMs However solving SVMs with monotonicity constraints will require even more computational time than SVMs In the era of big data information is ubiquitous The progress of data processing and analyzing the ability of computer hardware has fallen behind the growth of information With the size of dataset becoming larger and larger the efficiency of SVMs decreased gradually Therefore in this research a parallelized Conjugate Gradient (CG) strategy is proposed to solve the regularized monotonicity constrained SVMs Due to the characteristics of the CG method the dataset can be divided into n parts for parallel computing at different times This study proposed an RMC-SVMs with a parallel strategy to reduce the required training time and to increase the feasibility of using RMC-SVMs in real world applications

Date of Award	2016 Jul 22
Original language	English
Supervisor	Sheng-Tun Li (Supervisor)