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Hyperheuristic decision tree induction
 World Congress on Nature & Biologically Inspired Computing
, 2009
"... Abstract—Hyperheuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyperheuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class o ..."
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Abstract—Hyperheuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyperheuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class of problem instances). Hyperheuristics have been little explored in data mining. Here we apply a hyperheuristic approach to data mining, by searching a space of decision tree induction algorithms. The result of hyperheuristic search in this case is a new decision tree induction algorithm. We show that hyperheuristic search over a space of decision tree induction rules is able to find decision tree induction algorithms that outperform many different version of ID3 on unseen test sets. Keywords data mining, hyperheuristics, decision trees, evolutionary algorithm. I.
An Evolutionary Density and GridBased Clustering Algorithm
"... Abstract. This paper presents EDACluster, an Estimation of Distribution Algorithm (EDA) applied to the clustering task. EDA is an Evolutionary Algorithm used here to optimize the search for adequate clusters when very little is known about the target dataset. The proposed algorithm uses a mixed appr ..."
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Abstract. This paper presents EDACluster, an Estimation of Distribution Algorithm (EDA) applied to the clustering task. EDA is an Evolutionary Algorithm used here to optimize the search for adequate clusters when very little is known about the target dataset. The proposed algorithm uses a mixed approach – density and gridbased – to identify sets of dense cells in the dataset. The output is a list of items and their associated clusters. Items in lowdensity areas are considered noise and are not assigned to any cluster. This work uses four public domain datasets to perform the tests that compare EDACluster with DBSCAN, a conventional densitybased clustering algorithm. 1.
Under consideration for publication in Knowledge and Information Systems Evolving Rule Induction Algorithms with Multiobjective Grammarbased Genetic Programming
, 2007
"... Multiobjective optimization has played a major role in solving problems where two or more conflicting objectives need to be simultaneously optimized. This paper presents a MultiObjective Grammarbased Genetic Programming (MOGGP) system that automatically evolves complete rule induction algorithms, ..."
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Multiobjective optimization has played a major role in solving problems where two or more conflicting objectives need to be simultaneously optimized. This paper presents a MultiObjective Grammarbased Genetic Programming (MOGGP) system that automatically evolves complete rule induction algorithms, which in turn produce both accurate and compact rule models. The system was compared with a single objective GGP and three other rule induction algorithms. In total, 20 UCI data sets were used to generate and test generic rule induction algorithms, which can be now applied to any classification data set. Experiments showed that, in general, the proposed MOGGP finds rule induction algorithms with competitive predictive accuracies and more compact models than the algorithms it was compared with.
G Section: Evolutionary Algorithms Genetic Programming for Automatically Constructing Data Mining Algorithms
"... Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. ..."
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Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Automatically Evolving Rule Induction Algorithms Tailored to the Prediction of Postsynaptic Activity in Proteins
"... It is wellknown that no classification algorithm is the best in all application domains. The conventional approach for coping with this problem consists of trying to select the best classification algorithm for the target application domain. We propose a refreshing departure from this ∗Correspondin ..."
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It is wellknown that no classification algorithm is the best in all application domains. The conventional approach for coping with this problem consists of trying to select the best classification algorithm for the target application domain. We propose a refreshing departure from this ∗Corresponding author 1 approach, consisting of automatically creating a rule induction algorithm tailored to the target application domain. This work proposes a grammarbased genetic programming (GGP) system to perform “algorithm construction”. The GGP is used to build a complete rule induction algorithm tailored to 5 wellknown UCI data sets and a protein data set, where the goal is to predict whether or not a protein presents postsynaptic activity. The results show that the rule induction algorithms automatically constructed by the GGP are competitive with wellknown humandesigned rule induction algorithms. Moreover, in the postsynaptic case study, the GGP was more successful than the humandesigned algorithms in discovering accurate rules predicting the minority class – whose prediction is more difficult and tends to be more important to the user than the prediction of the majority class.
Research Article A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining
, 2008
"... We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary ..."
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We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal attribute values, a feature that current PSO and ACO rule induction algorithms lack. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 27 publicdomain, realworld data sets often used to benchmark the performance of classification algorithms. We compare the PSO/ACO2 algorithm to an industry standard algorithm PART and compare a reduced version of our PSO/ACO2 algorithm, coping only with continuous data, to our new classification algorithm forcontinuousdatabasedondifferential evolution. The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and that PSO/ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining—where the goal is to discover knowledge that is not only accurate but also comprehensible to the user. The results also show that the reduced PSO version for continuous attributes provides a slight increase in accuracy when compared to the differential evolution variant. Copyright © 2008 N. Holden and A. A. Freitas. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
Creating Rule Ensembles From AutomaticallyEvolved Rule Induction Algorithms
"... Abstract Ensembles are a set of classification models that, when combined, produce better predictions than when used by themselves. This chapter proposes a new evolutionary algorithmbased method for creating an ensemble of rule sets consisting of two stages. First, an evolutionary algorithm (more p ..."
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Abstract Ensembles are a set of classification models that, when combined, produce better predictions than when used by themselves. This chapter proposes a new evolutionary algorithmbased method for creating an ensemble of rule sets consisting of two stages. First, an evolutionary algorithm (more precisely, a genetic programming algorithm) is used to automatically create complete rule induction algorithms. Secondly, the automaticallyevolved rule induction algorithms are used to produce rule sets that are then combined into an ensemble. Concerning this second stage, we investigate the effectiveness of two different approaches for combining the votes of all rule sets in the ensemble and two different approaches for selecting which subset of evolved rule induction algorithms (out of all evolved algorithms) should be used to produce the rule sets that will be combined into an ensemble.