Negative Confidence-Aware Weakly Supervised Binary Classification for Effective Review Helpfulness Classification

The incompleteness of positive labels and the presence of many unlabelled instances are common problems in binary classification applications such as in review helpfulness classification. Various studies from the classification literature consider all unlabelled instances as negative examples. However, a classification model that learns to classify binary instances with incomplete positive labels while assuming all unlabelled data to be negative examples will often generate a biased classifier. In this work, we propose a novel Negative Confidence-aware Weakly Supervised approach (NCWS), which customises a binary classification loss function by discriminating the unlabelled examples with different negative confidences during the classifier's training. NCWS allows to effectively, unbiasedly identify and separate positive and negative instances after its integration into various binary classifiers from the literature, including SVM, CNN and BERT-based classifiers. We use the review helpfulness classification as a test case for examining the effectiveness of our NCWS approach. We thoroughly evaluate NCWS by using three different datasets, namely one from Yelp (venue reviews), and two from Amazon (Kindle and Electronics reviews). Our results show that NCWS outperforms strong baselines from the literature including an existing SVM-based approach (i.e. SVM-P), the positive and unlabelled learning-based approach (i.e. C-PU) and the positive confidence-based approach (i.e. P-conf) in addressing the classifier's bias problem. Moreover, we further examine the effectiveness of NCWS by using its classified helpful reviews in a state-of-the-art review-based venue recommendation model (i.e. DeepCoNN) and demonstrate the benefits of using NCWS in enhancing venue recommendation effectiveness in comparison to the baselines

Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching

Multi-class classification is mandatory for real world problems and one of promising techniques for multi-class classification is Error Correcting Output Code. We propose a method for constructing the Error Correcting Output Code to obtain the suitable combination of positive and negative classes encoded to represent binary classifiers. The minimum weight perfect matching algorithm is applied to find the optimal pairs of subset of classes by using the generalization performance as a weighting criterion. Based on our method, each subset of classes with positive and negative labels is appropriately combined for learning the binary classifiers. Experimental results show that our technique gives significantly higher performance compared to traditional methods including the dense random code and the sparse random code both in terms of accuracy and classification times. Moreover, our method requires significantly smaller number of binary classifiers while maintaining accuracy compared to the One-Versus-One.Comment: 7 pages, 3 figure