Global learning of decision trees by an evolutionary algorithm

Krȩtowski, Marek; Grzes, Marek

doi:10.1007/0-387-26325-x_36

Cited by 27 publications

(28 citation statements)

References 10 publications

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“…We showed that homogeneous trees (univariate [12] or oblique [13,14]) can be effectively induced and we demonstrated that globally generated classifiers are generally less complex with at least comparable accuracy. In this paper, we want to merge the two developed methods in one system, which will be able to induce mixed trees.…”

Section: Introductionmentioning

confidence: 84%

“…As the presented approach is a continuation and unification of our work on the global induction of homogeneous decision trees [12,13,14], in this section we described only these issues that are specific to mixed trees.…”

Section: Global Induction Of Mixed Decision Treesmentioning

confidence: 99%

“…It is also compared to two homogenous versions of our global GDT system: univariate -GDT-AP [12] and oblique GDT-OB [14]. All prepared artificial datasets comprise training and testing parts.…”

Section: Fig 1 Examples Of Artificial Datasetsmentioning

confidence: 99%

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Mixed Decision Trees: An Evolutionary Approach

Krȩtowski

Grzes

2006

Data Warehousing and Knowledge Discovery

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Abstract. In the paper, a new evolutionary algorithm (EA) for mixed tree learning is proposed. In non-terminal nodes of a mixed decision tree different types of tests can be placed, ranging from a typical univariate inequality test up to a multivariate test based on a splitting hyperplane. In contrast to classical top-down methods, our system searches for an optimal tree in a global manner, i.e. it learns a tree structure and tests in one run of the EA. Specialized genetic operators allow for generating new sub-trees, pruning existing ones as well as changing the node type and the tests. The proposed approach was experimentally verified on both artificial and real-life data and preliminary results are promising.

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Section: Introductionmentioning

confidence: 84%

Section: Global Induction Of Mixed Decision Treesmentioning

confidence: 99%

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Mixed Decision Trees: An Evolutionary Approach

Krȩtowski

Grzes

2006

Data Warehousing and Knowledge Discovery

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“…Therefore, direct representations of DTs are a natural representation of individuals with manageable effort required for the changes to genetic operators. In literature, direct representations of DTs are often used 27–29…”

Section: Construction Of Dts With Easmentioning

confidence: 99%

Evolutionary design of decision trees

Podgorelec

Šprogar

Pohorec

2012

WIREs Data Min & Knowl

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Decision tree (DT) is one of the most popular symbolic machine learning approaches to classification with a wide range of applications. Decision trees are especially attractive in data mining. It has an intuitive representation and is, therefore, easy to understand and interpret, also by nontechnical experts. The most important and critical aspect of DTs is the process of their construction. Several induction algorithms exist that use the recursive top‐down principle to divide training objects into subgroups based on different statistical measures in order to achieve homogeneous subgroups. Although being robust and fast, generally providing good results, their deterministic and heuristic nature can lead to suboptimal solutions. Therefore, alternative approaches have developed which try to overcome the drawbacks of classical induction. One of the most viable approaches seems to be the use of evolutionary algorithms, which can produce better DTs as they are searching for globally optimal solutions, evaluating potential solutions with regard to different criteria. We review the process of evolutionary design of DTs, providing the description of the most common approaches as well as referring to recognized specializations. The overall process is first explained and later demonstrated in a step‐by‐step case study using a dataset from the University of California, Irvine (UCI) machine learning repository. © 2012 Wiley Periodicals, Inc. This article is categorized under: Technologies > Classification Technologies > Machine Learning

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“…In the framework of univariate trees, most of the research was concentrated on global induction (e.g. [11,18,19,14]), whereas for linear trees mainly top-down methods were developed, where only splitting hyper-planes in internal nodes were evolutionary searched (e.g. [5,4,12]).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Learning of Linear Trees with Embedded Feature Selection

Krȩtowski

Grzes

2006

Artificial Intelligence and Soft Computing – ICAISC 2006

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Abstract. In the paper a new evolutionary algorithm for global induction of linear trees is presented. The learning process consists of searching for both a decision tree structure and hyper-plane weights in all nonterminal nodes. Specialized genetic operators are developed and applied according to the node quality and location. Feature selection aimed at simplification of the splitting hyper-planes is embedded into the algorithm and results in elimination of noisy and redundant features. The proposed approach is verified on both artificial and real-life data and the obtained results are promising.

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Global learning of decision trees by an evolutionary algorithm

Cited by 27 publications

References 10 publications

Mixed Decision Trees: An Evolutionary Approach

Mixed Decision Trees: An Evolutionary Approach

Evolutionary design of decision trees

Evolutionary Learning of Linear Trees with Embedded Feature Selection

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