Navegando por Palavras-chave "Complexity measures"

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    Data complexity meta-features for regression problems
    (Springer, 2018) Lorena, Ana C. [UNIFESP]; Maciel, Aron I. [UNIFESP]; de Miranda, Pericles B. C.; Costa, Ivan G.; Prudencio, Ricardo B. C.
    In meta-learning, classification problems can be described by a variety of features, including complexity measures. These measures allow capturing the complexity of the frontier that separates the classes. For regression problems, on the other hand, there is a lack of such type of measures. This paper presents and analyses measures devoted to estimate the complexity of the function that should fitted to the data in regression problems. As case studies, they are employed as meta-features in three meta-learning setups: (i) the first one predicts the regression function type of some synthetic datasets; (ii) the second one is designed to tune the parameter values of support vector regressors; and (iii) the third one aims to predict the performance of various regressors for a given dataset. The results show the suitability of the new measures to describe the regression datasets and their utility in the meta-learning tasks considered. In cases (ii) and (iii) the achieved results are also similar or better than those obtained by the use of classical meta-features in meta-learning.
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    Effect of label noise in the complexity of classification problems
    (Elsevier B.V., 2015-07-21) Garcia, Luis P. F.; Carvalho, Andre C. P. L. F. de; Lorena, Ana C. [UNIFESP]; Universidade de São Paulo (USP); Universidade Federal de São Paulo (UNIFESP)
    Noisy data are common in real-World problems and may have several causes, like inaccuracies, distortions or contamination during data collection, storage and/or transmission. the presence of noise in data can affect the complexity of classification problems, making the discrimination of objects from different classes more difficult, and requiring more complex decision boundaries for data separation. in this paper, we investigate how noise affects the complexity of classification problems, by monitoring the sensitivity of several indices of data complexity in the presence of different label noise levels. To characterize the complexity of a classification dataset, we use geometric, statistical and structural measures extracted from data. the experimental results show that some measures are more sensitive than others to the addition of noise in a dataset These measures can be used in the development of new preprocessing techniques for noise identification and novel label noise tolerant algorithms. We thereby show preliminary results on a new filter for noise identification, which is based on two of the complexity measures which were more sensitive to the presence of label noise. (C) 2015 Elsevier B.V. All rights reserved.
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    Noise detection in the meta-learning level
    (Elsevier Science Bv, 2016) Garcia, Luis P. F.; de Carvalho, Andre C. P. L. F.; Lorena, Ana C. [UNIFESP]
    The presence of noise in real data sets can harm the predictive performance of machine learning algorithms. There are several noise filtering techniques whose goal is to improve the quality of the data in classification tasks. These techniques usually scan the data for noise identification in a preprocessing step. Nonetheless, this is a non-trivial task and some noisy data can remain unidentified, while safe data can also be removed. The bias of each filtering technique influences its performance on a particular data set. Therefore, there is no single technique that can be considered the best for all domains or data distribution and choosing a particular filter is not straightforward. Meta-learning has been largely used in the last years to support the recommendation of the most suitable machine learning algorithm(s) for a new data set. This paper presents a meta-learning recommendation system able to predict the expected performance of noise filters in noisy data identification tasks. For such, a meta-base is created, containing meta-features extracted from several corrupted data sets along with the performance of some noise filters when applied to these data sets. Next, regression models are induced from this meta base to predict the expected performance of the investigated filters in the identification of noisy data. The experimental results show that meta-learning can provide a good recommendation of the most promising filters to be applied to new classification data sets. (C) 2015 Elsevier B.V. All rights reserved.