Bagging is a simple yet effective design which combines multiple single learners to form an ensemble for prediction. Despite its popular usage in many real-world applications, existing research is mainly concerned with studying unstable learners as the key to ensure the performance gain of a bagging predictor, with many key factors remaining unclear. For example, it is not clear when a bagging predictor can outperform a single learner and what is the expected performance gain when different learning algorithms were used to form a bagging predictor. In this paper, we carry out comprehensive empirical studies to evaluate bagging predictors by using 12 different learning algorithms and 48 benchmark data-sets. Our analysis uses robustness and stability decompositions to characterize different learning algorithms, through which we rank all learning algorithms and comparatively study their bagging predictors to draw conclusions. Our studies assert that both stability and robustness are key requirements to ensure the high performance for building a bagging predictor. In addition, our studies demonstrated that bagging is statistically superior to most single base learners, except for KNN and Naïve Bayes (NB). Multi-layer perception (MLP), Naïve Bayes Trees (NBTree), and PART are the learning algorithms with the best bagging performance.

We address the problem of building a continuous occupancy representation of the environment with ranging sensors. Observations from such sensors provide two types of information: a line segment or a beam indicating no returns along them (free-space); a point or return at the end of the segment representing an occupied surface. To model these two types of observations in a principled statistical manner, we propose a novel methodology based on integral kernels. We show that integral kernels can be directly incorporated into a Gaussian process classification (GPC) framework to provide a continuous non-parametric Bayesian estimation of occupancy. Directly handling line segment and point observations avoids the need to discretise segments into points, reducing the computational cost of GPC inference and learning. We present experiments on 2D and 3D datasets demonstrating the benefits of the approach.

The demand for computer-assisted language learning systems that can provide corrective feedback on language learners’ speaking has increased. However, it is not a trivial task to detect grammatical errors in oral conversations because of the unavoidable errors of automatic speech recognition systems. To provide corrective feedback, a novel method to detect grammatical errors in speaking performance is proposed. The proposed method consists of two sub-models: the grammaticality-checking model and the error-type classification model. We automatically generate grammatical errors that learners are likely to commit and construct error patterns based on the articulated errors. When a particular speech pattern is recognized, the grammaticality-checking model performs a binary classification based on the similarity between the error patterns and the recognition result using the confidence score. The error-type classification model chooses the error type based on the most similar error pattern and the error frequency extracted from a learner corpus. The grammaticality checking method largely outperformed the two comparative models by 56.36% and 42.61% in F-score while keeping the false positive rate very low. The error-type classification model exhibited very high performance with a 99.6% accuracy rate. Because high precision and a low false positive rate are important criteria for the language-tutoring setting, the proposed method will be helpful for intelligent computer-assisted language learning systems.

The increasing volume of information generated on micro-blogging sites such as Twitter raises several challenges to traditional text mining techniques. First, most texts from those sites are abbreviated due to the constraints of limited characters in one post; second, the input usually comes in streams of large-volumes. Therefore, it is of significant importance to develop effective and efficient representations of abbreviated texts for better filtering and mining. In this paper, we introduce a novel transfer learning approach, namely transfer latent semantic learning, that utilizes a large number of related tagged documents with rich information from other sources (source domain) to help build a robust latent semantic space for the abbreviated texts (target domain). This is achieved by simultaneously minimizing the document reconstruction error and the classification error of the labeled examples from the source domain by building a classifier with hinge loss in the latent semantic space. We demonstrate the effectiveness of our method by applying them to the task of classifying and tagging abbreviated texts. Experimental results on both synthetic datasets and real application datasets, including Reuters-21578 and Twitter data, suggest substantial improvements using our approach over existing ones.

The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification problems since they measure the performance of classifiers without making any specific assumptions about the class distribution and misclassification costs. This is desirable because the class distribution and misclassification costs may be unknown during training process or even change in environment. MAUC, the extension of AUC to multi-class problems, has also attracted a lot of attention. However, despite the emergence of approaches for training classifiers with large AUC, little has been done for MAUC. This paper analyzes MAUC in-depth, and reveals that the maximization of MAUC can be achieved by decomposing the multi-class problem into a number of independent sub-problems. These sub-problems are formulated in the form of a “learning to rank” problem, for which well-established methods already exist. Based on the analysis, a method that employs RankBoost algorithm as the sub-problem solver is proposed to achieve classification systems with maximum MAUC. Empirical studies have shown the advantages of the proposed method over other eight relevant methods. Due to the importance of MAUC to multi-class cost-sensitive learning and class imbalanced learning problems, the proposed method is a general technique for both problems. It can also be generalized to accommodate other learning algorithms as the sub-problem solvers.

In this paper, we propose a novel method to select the most informativesubset of features, which has little redundancy andvery strong discriminating power. Our proposed approach automaticallydetermines the optimal number of features and selectsthe best subset accordingly by maximizing the averagepairwise informativeness, thus has obvious advantage overtraditional filter methods. By relaxing the essential combinatorialoptimization problem into the standard quadratic programmingproblem, the most informative feature subset canbe obtained efficiently, and a strategy to dynamically computethe redundancy between feature pairs further greatly acceleratesour method through avoiding unnecessary computationsof mutual information. As shown by the extensive experiments,the proposed method can successfully select the mostinformative subset of features, and the obtained classificationresults significantly outperform the state-of-the-art results onmost test datasets.

ROC curves and cost curves are two popular ways of visualising classifier performance, finding appropriate thresholds according to the operating condition, and deriving useful aggregated measures such as the area under the ROC curve (AUC) or the area under the optimal cost curve. In this note we present some new findings and connections between ROC space and cost space, by using the expected loss over a range of operating conditions. In particular, we show that ROC curves can be transferred to cost space by means of a very natural way of understanding how thresholds should be chosen, by selecting the threshold such that the proportion of positive predictions equals the operating condition (either in the form of cost proportion or skew). We call these new curves {ROC Cost Curves}, and we demonstrate that the expected loss as measured by the area under these curves is linearly related to AUC. This opens up a series of new possibilities and clarifies the notion of cost curve and its relation to ROC analysis. In addition, we show that for a classifier that assigns the scores in an evenly-spaced way, these curves are equal to the Brier Curves. As a result, this establishes the first clear connection between AUC and the Brier score.

Today, mobile robots are increasingly expected to operate in ever more complex and dynamic environments.In order to carry out many of the higher level tasks envisioned a semantic understanding of a workspace is pivotal. Here our field has benefited significantly from successes in machine learning and vision: applications in robotics of off-the-shelf object detectors are plentiful. This paper outlines an online, any-time planning framework enabling the active exploration of such detections. Our approach exploits the ability to move to different vantage points and implicitly weighs the benefits of gaining more certainty about the existence of an object against the physical cost of the exploration required. The result is a robot which plans trajectories specifically to decrease the entropy of putative detections. Our system is demonstrated to significantly improve detection performance and trajectory length in simulated and real robot experiments.

LogAnswer is a question answering (QA) system for the German language, aimed at providing concise and correct answers to arbitrary questions. For this purpose LogAnswer is designed as an embedded artificial intelligence system which integrates methods from several fields of AI, namely natural language processing, machine learning, knowledge representation and automated theorem proving. We intend to employ LogAnswer as a virtual user within Internet-based QA forums, where it must be able to identify the questions that it cannot answer correctly, a task that normally receives little attention in QA research compared to the actual answer derivation. The paper presents a machine learning solution to the wrong answer avoidance (WAA) problem, applying a meta classifier to the output of simple term-based classifiers and a rich set of other WAA features. Experiments with a large set of real-world questions from a QA forum show that the proposed method significantly improves the WAA characteristics of our system.

Fashion magazines contain a number of photographs of fashion models, and their clothing coordinates serve as useful references. In this paper, we propose a recommender system for clothing coordinates using full-body photographs from fashion magazines. The task is that, given a photograph of a fashion item (e.g. tops) as a query, to recommend a photograph of other fashion items (e.g. bottoms) that is appropriate to the query. With the proposed method, we use a probabilistic topic model for learning information about coordinates from visual features in each fashion item region. We demonstrate the effectiveness of the proposed method using real photographs from a fashion magazine and two fashion style sharing services with the task of making top (bottom) recommendations given bottom (top) photographs.