Fuzzy partitioning of continuous attributes through discretization methods to construct fuzzy decision tree classifiers

Zeinalkhani, Mohsen; Eftekhari, Mohammad

doi:10.1016/j.ins.2014.03.087

Cited by 46 publications

(28 citation statements)

References 41 publications

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“…Lertworaprachaya et al (2014) suggested an intervalvalued fuzzy decision trees with optimal neighborhood perimeter Recently, Cappelli et al (2015) suggested a regime change analysis of imprecise time series (i.e., interval-valued time series) based on regression trees. Useful references on decision trees in a fuzzy framework which can be considered for future studies are, e.g., Suarez and Lutsko (1999), Chiang and Hsu (2002), Olaru and Wehenkel (2003), Qin and Lawry (2005), Wang et al (2008), Zeinalkani and Eftekhari (2014). --Three-way analysis: in the last decades, increasing attention has also been paid to fuzzy clustering models for complex structures of fuzzy data.…”

Section: Other Exploratory Multivariate Methods For Imprecise Datamentioning

confidence: 99%

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

D’Urso

2017

Granul. Comput.

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Section: Other Exploratory Multivariate Methods For Imprecise Datamentioning

confidence: 99%

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

D’Urso

2017

Granul. Comput.

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“…Another difference is that in FBDTs an attribute can appear several times in the same path. Both methods use the fuzzy information gain [21] for the attribute selection.…”

Section: B Fuzzy Decision Trees For Big Datamentioning

confidence: 99%

On the Usage of the Probability Integral Transform to Reduce the Complexity of Multi-Way Fuzzy Decision Trees in Big Data Classification Problems

Elkano

Uriz

Bustince

et al. 2018

2018 IEEE International Congress on Big Data (BigData Congress)

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We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems. The proposed algorithm builds a fixed number of fuzzy sets for all variables and adjusts their shape and position to the real distribution of training data. A two-step process is applied : 1) transformation of the original distribution into a standard uniform distribution by means of the probability integral transform. Since the original distribution is generally unknown, the cumulative distribution function is approximated by computing the q-quantiles of the training set; 2) construction of a Ruspini strong fuzzy partition in the transformed attribute space using a fixed number of equally distributed triangular membership functions. Despite the aforementioned transformation, the definition of every fuzzy set in the original space can be recovered by applying the inverse cumulative distribution function (also known as quantile function). The experimental results reveal that the proposed methodology allows the state-of-the-art multi-way fuzzy decision tree (FMDT) induction algorithm to maintain classification accuracy with up to 6 million fewer leaves.

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“…Fuzzy regression tree learning algorithms usually apply an initial discretization step aimed at generating fuzzy partitions on the continuous input variables, typically guided by some purposely-defined index [43,47]. Since this step has a direct impact on the performance of the learning algorithm, several studies evaluate how the accuracy and complexity of the generated models depend on the discretization [15,23,50].…”

Section: Introductionmentioning

confidence: 99%

“…A recent work [38] of one of the co-authors has proposed a novel fuzzy decision tree algorithm to address Big Data classification problems. First, the domain of the continuous input features is discretized by adopting a distributed approach based on the fuzzy entropy concept [50]. Then, the fuzzy decision tree is learned by applying a distributed learning algorithm based on the fuzzy information gain concept.…”

Section: Introductionmentioning

confidence: 99%

Building efficient fuzzy regression trees for large scale and high dimensional problems

et al. 2018

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Regression trees (RTs) are simple, but powerful models, which have been widely used in the last decades in different scopes. Fuzzy RTs (FRTs) add fuzziness to RTs with the aim of dealing with uncertain environments. Most of the FRT learning approaches proposed in the literature aim to improve the accuracy, measured in terms of mean squared error, and often neglect to consider the computation time and/or the memory requirements. In today's application domains, which require the management of huge amounts of data, this carelessness can strongly limit their use. In this paper, we propose a distributed FRT (DFRT) learning scheme for generating binary RTs from big datasets, that is based on the MapReduce paradigm. We have designed and implemented the scheme on the Apache Spark framework. We have used eight real-world and four synthetic datasets for evaluating its performance, in terms of mean squared error, computation time and scalability. As a baseline, we have compared the results with the distributed RT (DRT) and the Distributed Random Forest (DRF) available in the Spark MLlib library. Results show that our DFRT scales similarly to DRT and better than DRF. Regarding the performance, DFRT generalizes much better than DRT and similarly to DRF.

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Fuzzy partitioning of continuous attributes through discretization methods to construct fuzzy decision tree classifiers

Cited by 46 publications

References 41 publications

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

On the Usage of the Probability Integral Transform to Reduce the Complexity of Multi-Way Fuzzy Decision Trees in Big Data Classification Problems

Building efficient fuzzy regression trees for large scale and high dimensional problems

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