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The accuracy of time series forecasting can be increased by the employment of evolutionary systems. The improvement in the precision of such systems impact positively on the decision making process of many organizations. In this work we explore the decomposition of time series into linear and nonlinear patterns by the use of an autoregressive integrated moving average (ARIMA) method and a support...
Extracting topics from posts in social networks is a challenging and relevant computational task. Traditionally, topics are extracted by analyzing syntactic properties in the messages, assuming a high correlation between syntax and semantics. This work proposes SToC, a new method for generating more cohesive and meaningful semantic topics within a context. SToC post-processes the output of a Non-Negative...
Knowledge about algorithm similarity is an important aspect of meta-learning, where the information gathered from previous learning tasks can be used to guide the selection of algorithms for new datasets. Usually this task is done by comparing global performance measures across different datasets or alternatively, comparing the performance of algorithms at the instance-level. In both cases, the previous...
This paper evaluates some strategies to approximate the performance of dynamic ensembles based on NN-rule to the oracle performance. For this purpose, we use a multi-objective optimization algorithm, based on Differential Evolution, to generate automatically a pool of accurate and diverse classifiers in the form of Extreme Learning Machines. However, the rule defined for selecting the classifiers...
Studies with ensemble systems have gained attention recently and, most of them, propose new methods for the design (generation) of different components in these systems. In parallel, new contributions of meta-learning have been presented as an efficient alternative to automatic recommendation of algorithms. In this paper, we apply meta-learning in the process of recommendation of important parameters...
This paper proposes a study and comparison of the combination of multiple metadata types to improve the recommendation of movie items according to users' preferences. We used four algorithms available in the literature to analyze the descriptions, and compared each other using all the possible combinations of the metadata extracted from two datasets, namely MovieLens and IMDB. As a result of our evaluation,...
In multi-label datasets, the number of labels associated with each instance is an important feature to be observed. Two relevant characteristics related to datasets' number of labels are cardinality and density. In this work, we use artificial datasets generated through a framework named Mldatagem, freely-available in the internet. This framework enables configuring some other characteristics of the...
The rapid change of trading values from tangible assets to Intelectual Property has put both businesses and academia in a race to acquire and protect the rights to exploit such property. This is mainly accomplished in the form of patent issuing by the governments, being time consuming and complicated due to the vast amount of documents that need to be analyzed in order to assert the novelty or validity...
This paper proposes a collaborative filtering approach that uses users' reviews to produce item descriptions that represent a consensus of users regarding items' features. While earlier works focused on using structured metadata to represent items, recent approaches study how to use user-provided text, such as reviews, to produce better insights about the semantics in the content. Some involved problems,...
Recommender systems are designed to assist individuals to identify items of interest in a set of options. A context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems...
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