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In order to identify the inverse model for nonlinear dynamic systems, a multiple support vector machines (MSVM) based method was presented. According to their differential orders for the dynamic system, the input and output variables were allocated into multiple calculational subspaces. Taking advantage of its nonlinear regression performance, each subspace was represented by the least squares support...
Mining the interesting functions from the large scale data sets is an important task in KDD. Traditional gene expression programming (GEP) is a useful tool to discover functions. However, it cannot mine very complex functions. To resolve this problem, a novel method of function mining is proposed in this paper. The main contributions of this paper include: (1) analyzing the limitations of function...
This study applies clustering in population selection to improve the efficiency of evolutionary algorithms. The main contributions include: (a) Proposes a novel selection framework that uses the number of clusters for a population as the measurement the population diversity, (b) Proposes clustering-ranking selection, an instance of this framework, and discusses its mathematical principle by PD-SP...
Ensemble of classifiers is a learning paradigm where a set of classifiers are jointly used to improve the classification accuracy. The main contributions of this paper include: (1) proposing a new concept named attribute selection set based on gene expression programming (GEP), (2) analyzing the principle of classifier ensemble, (3) proposing an attribute-oriented ensemble classifier Based on niche...
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