2014
Authors
Cunha, T; Rossetti, RJF; Soares, C;
Publication
Modelling and Simulation 2014 - European Simulation and Modelling Conference, ESM 2014
Abstract
The huge amount of online information deprives the user to keep up with his/hers interests and preferences, Recommender Systems appeared to solve this problem, by employing social behavioural paradigms in order to recommend potentially interesting items to users, Among the several kinds of Recommender Systems, one of the most mature and most used in real world applications are known as Collaborative Filtering. These methods recommend items based on the preferences of similar-users, using only a user-item rating matrix. In this pa™ per we explain a methodology to use Multi™Agent based simulation to study the evolution of the data rating matrix and its effect on the performance of several Collaborative Filtering algorithms. Our results show that the best performing methods are user-based and item-based Collaborative Filtering and that the average algorithm performance is surprisingly constant for different rating schemes.
2014
Authors
Debiaso Rossi, ALD; de Leon Ferreira de Carvalho, ACPDF; Soares, C; de Souza, BF;
Publication
NEUROCOMPUTING
Abstract
Dynamic real-world applications that generate data continuously have introduced new challenges for the machine learning community, since the concepts to be learned are likely to change over time. In such scenarios, an appropriate model at a time point may rapidly become obsolete, requiring updating or replacement. As there are several learning algorithms available, choosing one whose bias suits the current data best is not a trivial task. In this paper, we present a meta-learning based method for periodic algorithm selection in time-changing environments, named MetaStream. It works by mapping the characteristics extracted from the past and incoming data to the performance of regression models in order to choose between single learning algorithms or their combination. Experimental results for two real regression problems showed that MetaStream is able to improve the general performance of the learning system compared to a baseline method and an ensemble-based approach.
2014
Authors
Miranda, PBC; Prudencio, RBC; de Carvalho, APLF; Soares, C;
Publication
NEUROCOMPUTING
Abstract
Support Vector Machines (SVMs) have achieved a considerable attention due to their theoretical foundations and good empirical performance when compared to other learning algorithms in different applications. However, the SVM performance strongly depends on the adequate calibration of its parameters. In this work we proposed a hybrid multi-objective architecture which combines meta-learning (ML) with multi-objective particle swarm optimization algorithms for the SVM parameter selection problem. Given an input problem, the proposed architecture uses a ML technique to suggest an initial Pareto front of SVM configurations based on previous similar learning problems; the suggested Pareto front is then refined by a multi-objective optimization algorithm. In this combination, solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of successful candidates, the search process would converge faster and be less expensive. In the performed experiments, the proposed solution was compared to traditional multi-objective algorithms with random initialization, obtaining Pareto fronts with higher quality on a set of 100 classification problems.
2014
Authors
Abreu, P; Soares, C; Camacho, R;
Publication
2014 14TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ITS APPLICATIONS (ICCSA)
Abstract
Optimization studies often require very large computational resources to execute experiments. Furthermore, most of the time, the experiments are repetitions (same problem instances and same algorithm with the same parameters) that were carried out in past studies. In this work, we propose a framework for the execution of optimization experiments in a distributed environment and for the storage of the results as well as of the experimental conditions. The framework can support not only the organized execution of experiments but it also enables the reuse of the results in future studies.
2014
Authors
Vanschoren, J; Brazdil, P; Soares, C; Kotthoff, L;
Publication
CEUR Workshop Proceedings
Abstract
2014
Authors
Sebastiao, R; Gama, J; Mendonca, T;
Publication
21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014)
Abstract
The emergence of real temporal applications under non-stationary scenarios has drastically altered the ability to generate and gather information. Nowadays, under dynamic scenarios, potentially unbounded and massive amounts of information are generated at high-speed rate, known as data streams. Dealing with evolving data streams imposes the online monitoring of data in order to detect changes. The contribution of this paper is to present the advantage of using fading histograms to compare data distribution for change detection purposes. In an windowing scheme, data distributions provided by the fading histograms are compared using the Kullback-Leibler divergence. The experimental results support that the detection delay time is smaller when using fading histograms to represent data instead of standard histograms.
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