In this paper an approach based on geneticprogramming for forecasting stochastic time series is outlined. To obtain a suitable test-bed some well-known time series are dressed with noise. The GP approach is endowed w...
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In this paper an approach based on geneticprogramming for forecasting stochastic time series is outlined. To obtain a suitable test-bed some well-known time series are dressed with noise. The GP approach is endowed with a multiobjective scheme relying on statistical properties of the faced series, i.e., on their momenta. Finally, the method is applied to the MIB30 Index series. (c) 2006 Elsevier B.V. All rights reserved.
Decision tree induction has been studied extensively in machine learning as a solution for classification problems. The way the linear decision trees partition the search space is found to be comprehensible and hence ...
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Decision tree induction has been studied extensively in machine learning as a solution for classification problems. The way the linear decision trees partition the search space is found to be comprehensible and hence appealing to data modelers. Comprehensibility is an important aspect of models used in medical data mining as it determines model credibility and even acceptability. In the practical sense though, inordinately long decision trees compounded by replication problems detracts from comprehensibility. This demerit can be partially attributed to their rigid structure that is unable to handle complex non-linear or/and continuous data. To address this issue we introduce a novel hybrid multivariate decision tree composed of polynomial, fuzzy and decision tree structures. The polynomial nature of these multivariate trees enable them to perform well in non-linear territory while the fuzzy members are used to squash continuous variables. By trading-off comprehensibility and performance using a multi-objective geneticprogramming optimization algorithm, we can induce polynomial-fuzzy decision trees (PFDT) that are smaller, more compact and of better performance than their linear decision tree (LDT) counterparts. In this paper we discuss the structural differences between PFDT and LDT (C4.5) and compare the size and performance of their models using medical data. (C) 2004 Elsevier B.V. All rights reserved.
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