Induction of decision trees using fuzzy partitions

Myles, Anthony J.; Brown, Steven D.

doi:10.1002/cem.816

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…These datasets are those used in the studies (Choi and Moon 2007;Li 2009;Myles and Brown 2003). We compare OFP_CLASS results with the ones obtained by the proposed methods in (Choi and Moon 2007;Li 2009;Myles and Brown 2003). All numeric attributes of these datasets are initially without discretizing.…”

Section: Methodsmentioning

confidence: 98%

“…• NFDT: In (Myles and Brown 2003), Myles and Brown introduce a method for the induction of fuzzy decision trees that determines the location and the associated uncertainty for each decision boundary during the construction process. In this process a fuzzy partition of attributes is generated that provides a confidence estimate of classification through membership propagation.…”

Section: Methodsmentioning

confidence: 99%

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OFP_CLASS: a hybrid method to generate optimized fuzzy partitions for classification

et al. 2011

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The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means of fuzzy sets, which constitute a fuzzy partition of the domains of these attributes. This method carries out a fuzzy discretization of continuous attributes in two stages. A fuzzy decision tree is used in the first stage to propose an initial set of crisp intervals, while a genetic algorithm is used in the second stage to define the membership functions and the cardinality of the partitions. After defining the fuzzy partitions, we evaluate and compare them with previously existing ones in the literature.

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Section: Methodsmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

OFP_CLASS: a hybrid method to generate optimized fuzzy partitions for classification

et al. 2011

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“…They are solved using a training set of samples, which are known a priori to belong to particular classes. The methods for unsupervised classification are mainly based on PCA decomposition followed by analysis of distances between classes, 136 construction of dendrograms, the use of the fuzzy set, 137 etc. Procrustean rotation 138 and Mahalanobis distance 139 ± 141 have been used for these purposes.…”

Section: Classification and Discriminationmentioning

confidence: 99%

Chemometrics: achievements and prospects

Rodionova

Pomerantsev

2006

Russ. Chem. Rev.

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The key chemometric methods and models used to solve The key chemometric methods and models used to solve the problems of qualitative and quantitative analysis and for the problems of qualitative and quantitative analysis and for process analytical technology are considered. The achievements in process analytical technology are considered. The achievements in the field of chemometrics made in the last 20 years are surveyed. the field of chemometrics made in the last 20 years are surveyed. The trends and prospects for its development are discussed. The The trends and prospects for its development are discussed. The bibliography includes 228 references bibliography includes 228 references. .

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“…In order to address this as a specific end goal, different learning techniques have been reported in literature for the generation of fuzzy set automatically. These techniques include decision tree [2,3,4], clustering [5,6], hybrid models [7,8,9] and evolutionary algorithms [10,11,12]. The presence of 3D-MF in T2FLS necessitates the adjustment of more parameters than T1FLS, which makes the learning process more complicated [13].…”

Section: Introductionmentioning

confidence: 99%