Application of Genetic Programming toInduction of Linear Classi cation
نویسنده
چکیده
A common problem in datamining is to nd accurate classi-ers for a dataset. For this purpose, genetic programming (GP) is applied to a set of benchmark classiication problems. Using GP, we are able to induce decision trees with a linear combination of variables in each function node. A new representation of decision trees using Strong Typing in GP is introduced. With this representation it is now possible to let the GP classify into any number of classes. Results indicate that GP can be applied successfully to classiication problems. Comparisons with current state-of-the-art algorithms in machine learning are presented and areas of future research are identiied.
منابع مشابه
Learning discriminant functions with fuzzy attributes for classification using genetic programming
Classi®cation is one of the important tasks in developing expert systems. Most of the previous approaches for classi®cation problem are based on classi®cation rules generated by decision trees. In this paper, we propose a new learning approach based on genetic programming to generate discriminant functions for classifying data. An adaptable incremental learning strategy and a distance-based ®tn...
متن کاملExtracting fuzzy classi cation rules with gene expression programming
In essence, data mining consists of extracting knowledge from data. This paper proposes an evolutionary system for discovering fuzzy classi cation rules. Fuzzy logic is useful for data mining especially in the case for performing classi cation task. Three methods were used to extract fuzzy classi cation rules using Evolutionary Algorithms: (1) genetic selection small number of large number of f...
متن کاملApplication of Genetic Programming to Induction of Linear Classi cation Trees
A common problem in datamining is to nd accurate classiiers for a dataset. For this purpose, genetic programming (GP) is applied to a benchmark of classiication problems. In particular, using GP we are able to induce decision trees with a linear combination of variables in each function node. The eeects of techniques as limited error tness, tness sharing Pareto scoring and domination Pareto sco...
متن کاملFrequency domain analysis of transient flow in pipelines; application of the genetic programming to reduce the linearization errors
The transient flow analyzing by the frequency domain method (FDM) is computationally much faster than the method of characteristic (MOC) in the time domain. FDM needs no discretization in time and space, but requires the linearization of governing equations and boundary conditions. Hence, the FDM is only valid for small perturbations in which the system’s hydraulics is almost linear. In this st...
متن کاملA Comparison of Genetic Programming and Neural Networks in Medical Data Analysis
We apply an interpreting variant of linear genetic programming to several diagnosis problems in medicine. We compare our results to results obtained with neural networks and argue that genetic programming is able to show similar performances in classi cation and generalization even when using a relatively small number of generations. Finally, an e cient algorithm for the elimination of introns ...
متن کامل