MMDT: a multi-valued and multi-labeled decision tree classifier for data mining

Chou, Shihchieh; Hsu, Chang-Ling

doi:10.1016/j.eswa.2004.12.035

Cited by 31 publications

(22 citation statements)

References 13 publications

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“…The existing methods which measure the goodness of each attribute are 'weighted similarity' [11] and 'weighted label ratio' [12]. Weighted similarity chooses the attribute for partitioning a data set into more similar subsets.…”

Section: The Evaluated Methods Of the Best Attributementioning

confidence: 99%

“…At present, in [11][12][13][14] the same conditions for the corresponding node to stop splitting is used.…”

Section: Definitionmentioning

confidence: 99%

“…Because for two label-sets in a given candidate label-sets, the proportion of labels in the candidate labelsets that do not appear in both of two label-sets also reflects similarity to some extent and it is known as global similarity of the set. According to global similarity, Chou [12] used the following formula to evaluate the similarity between two label-sets:…”

Section: To Select the Attribute For Splittingmentioning

confidence: 99%

“…The existing decision tree algorithms for dealing with multi-valued and multi-labeled data use the similarity of labels of each child node as the evaluation standard of an attribute classification effects [11][12][13][14].…”

Section: To Select the Attribute For Splittingmentioning

confidence: 99%

“…Chen [11] firstly proposed a multi-valued and multi-labeled decision tree classifier named MMC, and the algorithm used the similarity of labels of each child node as the evaluation standard of an attribute classification effects. Chou [12] proposed a new algorithm named MMDT based on MMC. The algorithm improved the similarity formula in MMC and the new formula for calculating similarity could examine the sum of the number of labels of the class set that appear in the two label sets and the number of labels of the class set that do not appear in the two label-sets.…”

Section: Introductionmentioning

confidence: 99%

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Multi-valued attribute and multi-labeled data decision tree algorithm

Liu

2011

Int. J. Mach. Learn. & Cyber.

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We present a novel approach t o the challenging issue of database conndential data protection. We adopt the decision tree framework as our baseline and extend it to cope with databases where the class label attribute is not speciied. We are interested in conndential data that are randomly distributed over diierent attributes referred to as multi-dimensional inference. For con-dential data protection, our method referred to as adaptive modiication mitigates inference by evaluating and modifying some, not all, relevant data records. We localize data modiication in a decision tree and, instead of exhaustively evaluating all modiication possibilities , we select informative data to modify. Our proposed method is eeective in protection of connden-tial data and scalable for handling large databases.

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Section: The Evaluated Methods Of the Best Attributementioning

confidence: 99%

“…At present, in [11][12][13][14] the same conditions for the corresponding node to stop splitting is used.…”

Section: Definitionmentioning

confidence: 99%

Section: To Select the Attribute For Splittingmentioning

confidence: 99%

Section: To Select the Attribute For Splittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-valued attribute and multi-labeled data decision tree algorithm

Liu

2011

Int. J. Mach. Learn. & Cyber.

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Multilabel feature selection: A comprehensive review and guiding experiments

Kashef

Nezamabadi‐pour

Nikpour

2018

WIREs Data Min & Knowl

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Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240 This article is categorized under: Technologies > Data Preprocessing

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Research on Multi-valued and Multi-labeled Decision Trees

Zhao

Chen

et al. 2006

Advanced Data Mining and Applications

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MMDT: a multi-valued and multi-labeled decision tree classifier for data mining

Cited by 31 publications

References 13 publications

Multi-valued attribute and multi-labeled data decision tree algorithm

Multi-valued attribute and multi-labeled data decision tree algorithm

Multilabel feature selection: A comprehensive review and guiding experiments

Research on Multi-valued and Multi-labeled Decision Trees

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