Evolutionary model trees for handling continuous classes in machine learning

Barros, Rodrigo C.; Ruiz, Duncan D.; Basgalupp, Márcio P.

doi:10.1016/j.ins.2010.11.010

Cited by 45 publications

(31 citation statements)

References 26 publications

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“…Different strategies were employed for deriving accurate DTs, such as bottom-up induction [2], hybrid induction [22], evolutionary induction [1,3] and ensemble of trees [4], just to name a few. Nevertheless, no strategy has been more successful in generating accurate and comprehensible decision trees with low computational effort than the greedy top-down induction strategy.…”

Section: Introductionmentioning

confidence: 99%

A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

Barros

Basgalupp

Carvalho

et al. 2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years.This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to the traditional decision-tree algorithms C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.

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Section: Introductionmentioning

confidence: 99%

A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

Barros

Basgalupp

Carvalho

et al. 2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

Self Cite

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“…Furthermore, they affirm that it is natural to use a tree structure to represent decision trees and that the mutation-crossover operators can be efficiently altered to match this structure. For other tree-encoding scheme examples, see [22], [28], [40]- [47].…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

A Survey of Evolutionary Algorithms for Decision-Tree Induction

Barros

Basgalupp

Carvalho

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

296

139

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Abstract-This paper presents a survey of evolutionary algorithms designed for decision tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divideand-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision tree classifiers. The paper original contributions are the following. First, it provides an upto-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy which addresses works that evolve decision trees and works that design decision tree components using evolutionary algorithms. Finally, a number of references is provided that describe applications of evolutionary algorithms for decision tree induction in different domains. The paper ends by addressing some important issues and open questions that can be subject of future research.

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“…C. Barros, D. D. Ruiz, and M. P. Basgalupp [21] suggested use of Model Trees. These trees are similar to regression trees, which are hierarchical structures for predicting continuous dependent variables.…”

Section: Literature Surveymentioning

confidence: 99%

“…The motivation behind hyper-heuristics is to raise the level of generality at which search methodologies can operate. In the context of decision trees, instead of having an evolutionary algorithm searching for the best decision tree to a given problem (a regular meta-heuristic approach [21][25], the generality level is raised by having an evolutionary algorithm searching for the best decision-tree induction algorithm that may be effectively applied to several different classification problems (a hyper-heuristic approach). …”

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confidence: 99%

A Survey on: Stratified mapping of Microarray Gene Expression datasets to decision tree algorithm aided through Evolutionary Design

V.Bhatambarekar¹,

Kulkarni²

2014

IOSRJCE

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Analyzing gene expression data is a challenging task since the large number of features against theshortage of available examples can be prone to over fitting. In order to avoid this pitfall and achieve high performance, some approaches construct complex classifiers, using new or well-established strategies. In medical decision making (classification, diagnosing) there are many situations where decision must be made effectively and reliably. Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. Following recent breakthroughs in the automatic design of machine learning algorithms, a novel approach of hyper-heuristic evolutionary algorithm called HEAD-DT that evolves design components of top-down decision tree induction algorithms can be used to improve the classification accuracy. Keywords: Automatic algorithm design, Decision trees, Evolutionary Algorithms, Genetic programming, Hyper-heuristics, Machine Learning, Random forest I. Introdution:The DNA microarray technology allows monitoring the expression of thousands of genes simultaneously [1] .Thus, it can lead to better understanding of many biological processes, improved diagnosis, and treatment of several diseases. However data collected by DNA microarray's are not suitable for direct human analysis, since a single experiment contains thousands of measured expression values. Several approaches have been suggested towards exploiting data mining from microarray data [2] including supervised and unsupervised machine learning algorithms where supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples [3]. Decision tree is a classifier depicted in a flowchart like tree structure which has been widely used to represent classification models, due to its comprehensible nature that resembles the human reasoning. Decision tree induction algorithms present several advantages over other learning algorithms, such as robustness to noise, low computational cost for generating the model, and ability to deal with redundant attributes. Besides, the induced model usually presents a good generalization ability [3] [4]. Most decision tree induction algorithms are based on a greedy top...

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Evolutionary model trees for handling continuous classes in machine learning

Cited by 45 publications

References 26 publications

A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

A Survey of Evolutionary Algorithms for Decision-Tree Induction

A Survey on: Stratified mapping of Microarray Gene Expression datasets to decision tree algorithm aided through Evolutionary Design

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