Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:APPLIED INTELLIGENCE
Key Words:Transfer learning Ensemble selection Rank-based ensemble pruning Rank-based Reduce Error (RankRE) ensemble selection approach Transfer learning algorithm based on rankRE (RankRE-TL)
Abstract:A major assumption in traditional machine leaning is that the training and testing data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. In recent years, transfer learning has emerged as a new learning paradigm to cope with this considerable challenge. It focuses on exploiting previously learnt knowledge by leveraging information from an old source domain to help learning in a new target domain. In this work, we integrate the knowledge-leverage-based Transfer Learning mechanism with a Rank-based Reduce Error ensemble selection approach to fulfill the transfer learning task, called RankRE-TL. Ensemble selection is important for improving both efficiency and predictive accuracy of an ensemble system. It aims to select a proper subset of the whole ensemble, which usually outperforms the whole one. Therefore, we appropriately modify the Reduce Error (RE) pruning technique and design a new Rank-based Reduce Error ensemble selection method (RankRE) to deal with the transfer learning task. The design idea of RankRE is to find the candidate classifier which is expected to improve the classification performance of the extended subensemble the most. In the RankRE-TL algorithm, the initial Support Vector Machine (SVM) ensemble is learnt based upon dynamic training dataset regrouping. And simultaneously, a new construction method of validation set is designed for RankRE-TL, which differs from the method used in conventional ensemble selection paradigm.
ISSN No.:0924-669X
Translation or Not:no
Date of Publication:2018-08-01
Co-author:李美岭
Correspondence Author:dq
Date of Publication:2018-08-01