We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim of the sampling strategy is to overcome the curse of long-tailed class distributions exhibited in majority of large-scale multi-class classification problems and to reduce the number of pairs of examples in the expanded data. We show that this strategy does not alter the consistency of the empirical risk minimization principle defined over the double sample reduction. Experiments are carried out on DMOZ and Wikipedia collections with 10,000 to 100,000 classes where we show the efficiency of the proposed approach in terms of training and prediction time, memory consumption, and predictive performance with respect to state-of-the-art approaches.

In this paper, we study flat and hierarchical classification strategies in the context of large-scale taxonomies. Addressing the problem from a learning-theoretic point of view, we first propose a multi-class, hierarchical data dependent bound on the generalization error of classifiers deployed in large-scale taxonomies. This bound provides an explanation to several empirical results reported in the literature, related to the performance of flat and hierarchical classifiers. Based on this bound, we also propose a technique for modifying a given taxonomy through pruning, that leads to a lower value of the upper bound as compared to the original taxonomy. We then present another method for hierarchy pruning by studying approximation error of a family of classifiers, and derive from it features used in a meta-classifier to decide which nodes to prune. We finally illustrate the theoretical developments through several experiments conducted on two widely used taxonomies.

BACKGROUND: This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. RESULTS: The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PUBMED documents with MESH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MESH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BIOASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. CONCLUSIONS: A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MESH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PUBMED Central; produce "exact" and paragraph-sized "ideal" answers (summaries). The results of the systems that participated in the 2013 BIOASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM's MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the "ideal" answers; hence, they produced high quality summaries as answers. Overall, BIOASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.

For large-scale category systems, such as Directory Mozilla, which consist of tens of thousand categories, it has been empirically verified in earlier studies that the distribution of documents among categories can be modeled as a power-law distribution. It implies that a significant fraction of categories, referred to as rare categories, have very few documents assigned to them. This characteristic of the data makes it harder for learning algorithms to learn effective decision boundaries which can correctly detect such categories in the test set. In this work, we exploit the distribution of documents among categories to (i) derive an upper bound on the accuracy of any classifier, and (ii) propose a ranking-based algorithm which aims to maximize this upper bound. The empirical evaluation on publicly available large-scale datasets demonstrate that the proposed method not only achieves higher accuracy but also much higher coverage of rare categories as compared to state-of-the-art methods.

This paper provides an overview of the workshop Web-Scale Classification: Web Classification in the Big Data Era which was held in New York City, on February 28th as a workshop of the seventh International Conference on Web Search and Data Mining. The goal of the workshop was to discuss and assess recent research focusing on classification and mining in Web-scale category systems. The workshop brought together members of several communities such web mining, machine learning, text classification and social media mining.

We study in this paper flat and hierarchical classification strategies in the context of large-scale taxonomies. To this end, we first propose a multiclass, hierarchical data dependent bound on the generalization error of classifiers deployed in large-scale taxonomies. This bound provides an explanation to several empirical results reported in the literature, related to the performance of flat and hierarchical classifiers. We then introduce another type of bounds targeting the approximation error of a family of classifiers, and derive from it features used in a meta-classifier to decide which nodes to prune (or flatten) in a large-scale taxonomy. We finally illustrate the theoretical developments through several experiments conducted on two widely used taxonomies.

While multi-class categorization of documents has been of research interest for over a decade, relatively fewer approaches have been proposed for large scale taxonomies in which the number of classes range from hundreds of thousand as in Directory Mozilla to over a million in Wikipedia. As a result of ever increasing number of text documents and images from various sources, there is an immense need for automatic classification of documents in such large hierarchies. In this paper, we analyze the tradeoffs between the important characteristics of different classifiers employed in the top down fashion. The properties for relative comparison of these classifiers include, (i) accuracy on test instance, (ii) training time (iii) size of the model and (iv) test time required for prediction. Our analysis is motivated by the well known error bounds from learning theory, which is also further reinforced by the empirical observations on the publicly available data from the Large Scale Hierarchical Text Classification Challenge. We show that by exploiting the data heterogenity across the large scale hierarchies, one can build an overall classification system which is approximately 4 times faster for prediction, 3 times faster to train, while sacrificing only 1% point in accuracy.

Focused crawlers are programs that wander in the Web, using its graph structure, and gather pages that belong to a specific topic. The most critical task in Focused Crawling is the scoring of the URLs as it designates the path that the crawler will follow, and thus its effectiveness. In this paper we propose a novel scheme for assigning scores to the URLs, based on the Reinforcement Learning (RL) framework. The proposed approach learns to select the best classifier for ordering the URLs. This formulation reduces the size of the search space for the RL method and makes the problem tractable. We evaluate the proposed approach on-line on a number of topics, which offers a realistic view of its performance, comparing it also with a RL method and a simple but effective classifier-based crawler. The results demonstrate the strength of the proposed approach.

Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. A number of ensemble selection methods that are based on greedy search of the space of all possible ensemble subsets have recently been proposed. This paper contributes a novel method, based on a new diversity measure that takes into account the strength of the decision of the current ensemble. Experimental comparison of the proposed method, dubbed Focused Ensemble Selection (FES), against state-of-the-art greedy ensemble selection methods shows that it leads to small ensembles with high predictive performance.

Multiagent learning attracts much attention in the past few years as it poses very challenging problems. Reinforcement Learning is an appealing solution to the problems that arise to Multi Agent Systems (MASs). This is due to the fact that Reinforcement Learning is a robust and well suited technique for learning in MASs. This paper proposes a multi-agent Reinforcement Learning approach, that uses coordinated actions, which we call strategies and a voting process that combines the decisions of the agents, in order to follow a strategy. We performed experiments to the predator-prey domain, comparing our approach with other multi-agent Reinforcement Learning techniques, getting promising results.