Boosting adaptive linear weak classifiers for online learning and tracking

Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak classifiers/features to adapt with the change over time. We are proposing a new online boosting algorithm where the form of the weak classifiers themselves are modified to cope with scene changes. Instead of replacement, the parameters of the weak classifiers are altered in accordance with the new data subset presented to the online boosting process at each time step. Thus we may avoid altogether the issue of how many weak classifiers to be replaced to capture the change in the data or which efficient search algorithm to use for a fast retrieval of weak classifiers. A computationally efficient method has been used in this paper for the adaptation of linear weak classifiers. The proposed algorithm has been implemented to be used both as an online learning and a tracking method. We show quantitative and qualitative results on both UCI datasets and several video sequences to demonstrate improved performance of our algorithm. 1

Algorithms for Sparse Linear Classifiers in the Massive Data Setting

Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classification problems in high dimensions. However, current algorithms for training sparse classifiers typically scale quite unfavorably with respect to the number of training examples. This paper proposes online and multi-pass algorithms for training sparse linear classifiers for high dimensional data. These algorithms have computational complexity and memory requirements that make learning on massive data sets feasible. The central idea that makes this possible is a straightforward quadratic approximation to the likelihood function

Using Machine Learning to Predict the Sentiment of Online Reviews: A New Framework for Comparative Analysis

Online reviews are becoming increasingly important for decision-making. Consumers often refer to online reviews for opinions before making a purchase. Marketers also acknowledge the importance of online reviews and use them to improve product success. However, the massive amount of online review data, as well as its unstructured nature, is a challenge for anyone wanting to derive a conclusion quickly. In this paper, we propose a novel framework for gauging the ratings of online reviews using machine learning techniques. This framework uses a combination of text pre-processing and feature extraction methods. Here, we investigate four different aspects of the new framework. First, we assess the performance of single and ensemble classifiers in predicting sentiment—positive or negative—initially on a specific dataset (Yelp), but subsequently also on two other datasets (Amazons product reviews and a movie review dataset). Second, using the best identified classifiers, we improve the accuracy with which neutral polarity can be predicted, an ability largely overlooked in the literature. Third, we further improve the performance of these classifiers by testing different pre-processing and feature extraction methods. Finally, we measure how well our deep learning approach performs on the same task compared to the best previously identified classifiers. Our extensive testing shows that the linear-kernel support vector machine, logistic regression and multilayer perceptron are the three best single classifiers in terms of accuracy, precision, recall, and F-measure. Their performance could be further improved if they were used as base classifiers for ensemble models. We also observe that several text pre-processing techniques—negation word identification, word elongation correction, and part of speech lemmatisation (combined with Terms Frequency and N-gram words)—can increase accuracy. In addition, we demonstrate that the general sentiment of lexicons such as SentiWordNet 3.0 and SenticNet 4 can be used to generate features with good results, although deep learning models can perform equally well. Experiments with different datasets confirm that our framework provides consistent outcomes. In particular, we have focused on improving the accuracy of neutral sentiment, and we conclude by showing how this can be achieved without sacrificing the accuracy of positive or negative ratings

Predicting Rating Polarity through Automatic Classification of Review Texts

Online reviews and ratings are important for potential customers when deciding whether to purchase a product or service. However, reading and synthesizing the massive amount of review data, which is often unstructured, is a huge challenge. In this study, we investigate the use of machine learning models to predict rating polarity (positive, neutral or negative) through automatic classification of review texts. We apply various single and ensemble classifiers to identify rating polarity of reviews from the 2017 Yelp dataset. Experimental results show that the linear kernel Support Vector Machine, Logistic Regression and Multilayer Perceptron are among the three best single classifiers in terms of accuracy, precision, recall and F-measure. Their performances can be further improved when used as base classifiers for ensemble models