A semi-supervised multi-label classification framework with feature reduction and enrichment

Abstract

2017) A semi-supervised multi-label classification framework with feature reduction and enrichment, Journal of Information and Telecommunication, 1:4, 305-318, ABSTRACTMulti-label classification (MLC) has drawn much attention thanks to its usefulness and omnipresence in real-world applications in which objects may be characterized by more than one label as in the traditional approach. Getting multi-label examples is costly and time-consuming; therefore, semi-supervised learning approach should be considered to take advantages of both labelled and unlabelled data. In this work, we propose a semi-supervised MLC algorithm exploiting the specific features of the prominent class label(s) chosen by a greedy approach as an extension of the LIFT algorithm, and unlabelled data consumption mechanism from the TESC algorithm. We also make a semi-supervised MLC application framework for Vietnamese texts with several feature enrichment steps including (a) a stage of enriching features by adding hidden topic features and (b) a stage of dimensional reduction for subtracting irrelevant features. Experimental results on a dataset of hotel reviews (for tourism) indicate that a reasonable amount of unlabelled data helps to increase the F1 score. Interestingly, with a small amount of labelled data, our algorithm can reach a comparative performance to the case of using a larger amount of labelled data. ARTICLE HISTORY

Distributed image retrieval with colour and keypoint features

“…The mechanism used for database image indexing is depicted in details in Korytkowski et al (2016) and Korytkowski (2017). Usually, classifiers are used for the purposes they are intended (Hoang, 2017;Pham, Nguyen, Tran, Nguyen, & Ha, 2017), but in the paper, we use weak classifiers to obtain distinctive features for a given visual class. Currently, deep learning-based approaches (Bologna & Hayashi, 2017;Chang, Constante, Gordon, & Singana, 2017) are gaining popularity in image analysis, but they are slower than the proposed approach and not suitable for relational database purposes.…”