Application of decision trees and multivariate regression trees in design and optimization

Forouraghi, Babak

doi:10.31274/rtd-180813-10075

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…To construct a decision tree for a respective decision class, the original training instances need to be re-assign into two revised decision classes, positive and negative (Liu et al 2000). On the other hand, a transformation with loss of information such as principle component analysis (Kendall 1980, Forouraghi et al 1994) could overlook useful knowledge.…”

Section: Mdti Learningmentioning

confidence: 98%

MFILM: a multi-dimensional fuzzy inductive learning method

Chen

Jeng

2005

Journal of Experimental & Theoretical Artificial Intelligen

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Inductive learning that creates a decision tree from a set of existing examples is shown to be useful for automated knowledge acquisition. Most of the existing methods however, handle only single-dimensional decision problems. Only some methods can deal with multi-dimensional decision problems. However, they are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a multidimensional fuzzy inductive learning method that integrates the fuzzy set theory into the conventional multi-dimensional decision tree induction methods. The method converts a multi-dimensional decision tree into a fuzzy multi-dimensional decision tree in which hurdle values for splitting branches and classes associated with leaves are fuzzy. Results from empirical tests indicate that the new fuzzy approach outperforms the other conventional methods.

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Section: Mdti Learningmentioning

confidence: 98%

MFILM: a multi-dimensional fuzzy inductive learning method

Chen

Jeng

2005

Journal of Experimental & Theoretical Artificial Intelligen

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“…This tendency causes a fragmentation problem [11]: each class value has only a few training examples in a split node at the bottom of a decision tree, and appropriate selection of an attribute would be difficult. A transformation with loss of information such as principle component analysis [6,7] could overlook useful knowledge.…”

Section: Introductionmentioning

confidence: 99%

Bloomy Decision Tree for Multi-objective Classification

Suzuki

Gotoh

Choki

2001

Principles of Data Mining and Knowledge Discovery

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This paper presents a novel decision-tree induction for a multi-objective data set, i.e. a data set with a multi-dimensional class. Inductive decision-tree learning is one of the frequently-used methods for a single-objective data set, i.e. a data set with a single-dimensional class. However, in a real data analysis, we usually have multiple objectives, and a classifier which explains them simultaneously would be useful and would exhibit higher readability. A conventional decision-tree inducer requires transformation of a multi-dimensional class into a singledimensional class, but such a transformation can considerably worsen both accuracy and readability. In order to circumvent this problem we propose a bloomy decision tree which deals with a multi-dimensional class without such transformations. A bloomy decision tree has a set of split nodes each of which splits examples according to their attribute values, and a set of flower nodes each of which predicts a class dimension of examples. A flower node appears not only at the fringe of a tree but also inside a tree. Our pruning is executed during tree construction, and evaluates each class dimension based on Cramér's V. The proposed method has been implemented as D3-B (Decision tree in Bloom), and tested with eleven data sets. The experiments showed that D3-B has higher accuracies in nine data sets than C4.5 and tied with it in the other two data sets. In terms of readability, D3-B has a smaller number of split nodes in all data sets, and thus outperforms C4.5.

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Application of decision trees and multivariate regression trees in design and optimization

Abstract: Introduction ID3-Type Algorithms Incremental ID3-Type Algorithms IDea Empirical Results Conclusions References IMPROVING NEURAL LEARNING THROUGH ELIMINATION OF REDUNDANCIES IN TRAINING EXAMPLES Abstract Introduction IDea Improving Neural Learning with IDea 42 Conclusions 48 References 49

Cited by 2 publications

References 54 publications

MFILM: a multi-dimensional fuzzy inductive learning method

MFILM: a multi-dimensional fuzzy inductive learning method

Bloomy Decision Tree for Multi-objective Classification

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