Improving sentiment analysis through ensemble learning of meta-level features

In this research, the well-known microblogging site, Twitter, was used for a sentiment analysis investigation. We propose an ensemble learning approach based on the meta-level features of seven existing lexicon resources for automated polarity sentiment classification. The ensemble employs four base learners (a Two-Class Support Vector Machine, a Two-Class Bayes Point Machine, a Two-Class Logistic Regression and a Two-Class Decision Forest) for the classification task. Three different labelled Twitter datasets were used to evaluate the effectiveness of this approach to sentiment analysis. Our experiment shows that, based on a combination of existing lexicon resources, the ensemble learners minimize the error rate by avoiding poor selection from stand-alone classifiers

Predicting Drug Targets from Heterogeneous Spaces using Anchor Graph Hashing and Ensemble Learning

© 2018 IEEE. The in silico prediction of potential drug-targetinteractions is of critical importance in drug research. Existing computational methods have achieved remarkable prediction accuracy, however usually obtain poor prediction efficiency due to computational problems. To improve the prediction efficiency, we propose to predict drug targets based on inte- gration of heterogeneous features with anchor graph hashing and ensemble learning. First, we encode each drug as a 5682- bit vector, and each target as a 4198-bit vector using their heterogeneous features respectively. Then, these vectors are embedded into low-dimensional Hamming Space using anchor graph hashing. Next, we append hashing bits of a target to hashing bits of a drug as a vector to represent the drug-target pair. Finally, vectors of positive samples composed of known drug-target pairs and randomly selected negative samples are used to train and evaluate the ensemble learning model. The performance of the proposed method is evaluated on simulative target prediction of 1094 drugs from DrugBank. Ex- tensive comparison experiments demonstrate that the proposed method can achieve high prediction efficiency while preserving satisfactory accuracy. In fact, it is 99.3 times faster and only 0.001 less in AUC than the best literature method 'Pairwise Kernel Method'