Attribute reduction: a horizontal data decomposition approach

Hońko, Piotr

doi:10.1007/s00500-014-1554-8

Cited by 13 publications

(3 citation statements)

References 21 publications

(27 reference statements)

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“…Make the hardware implementation scalable so that it can be used for data sets with arbitrary number of objects. The horizontal data decomposition approach proposed in [8] is to be used for this purpose.…”

Section: Hardware Implementationmentioning

confidence: 99%

Breadth search strategies for finding minimal reducts: towards hardware implementation

Choromański

Grzes

Hońko

2020

Neural Comput & Applic

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Attribute reduction, being a complex problem in data mining, has attracted many researchers. The importance of this issue rises due to ever-growing data to be mined. Together with data growth, a need for speeding up computations increases. The contribution of this paper is twofold: (1) investigation of breadth search strategies for finding minimal reducts in order to emerge the most promising method for processing large data sets; (2) development and implementation of the first hardware approach to finding minimal reducts in order to speed up time-consuming computations. Experimental research showed that for software implementation blind breadth search strategy is in general faster than frequency-based breadth search strategy not only in finding all minimal reducts but also in finding one of them. An inverse situation was observed for hardware implementation. In the future work, the implemented tool is to be used as a fundamental module in a system to be built for processing large data sets.Keywords Attribute reduction Á Minimal reducts Á Breadth search strategy Á Hardware implementation Á Field programmable gate arrays Neural Computing and Applications https://doi.org/10.1007/s00521-020-04833-7( 0123456789().,-volV) (0123456789(). ,-volV)Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Neural Computing and Applications

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Section: Hardware Implementationmentioning

confidence: 99%

Breadth search strategies for finding minimal reducts: towards hardware implementation

Choromański

Grzes

Hońko

2020

Neural Comput & Applic

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“…To struggle with the problem of memory capacity limitation, data can be decomposed into smaller portions, which are then processed separately . In general, data can be split with respect to objects (horizontal data decomposition) or attributes (vertical data decomposition).…”

Section: Introductionmentioning

confidence: 99%

“…1 To struggle with the problem of memory capacity limitation, data can be decomposed into smaller portions, which are then processed separately. [2][3][4] In general, data can be split with respect to objects (horizontal data decomposition) or attributes (vertical data decomposition). Horizontal decomposition, in contrast to the vertical one, produces data subsets that are complete in terms of characteristics of objects (ie, all attributes are stored in each subset).…”

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confidence: 99%

Binary classification rule generation from decomposed data

Hońko

2019

Int J Intell Syst

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Learning classification rules from data that do not fit in the available memory is a challenging task. The goal of this study is to develop an approach for generating binary classification rules from decomposed data that are equivalent in terms of quality to those found over the whole data. In the proposed approach, each class is divided into the same arbitrary small number of subtables. For each pair of subsets from different classes, rule sets are induced using any sequential covering algorithm. Rule sets generated from the same positive class subset and different negative class subsets are merged using an operator constructed on the basis of Cartesian product and conjunction operators. The rule sets obtained in this way are joined into one set. During the rule merging, unnecessary rules are removed. It is proven that for training data, the quality of the rule set generated using the approach is the same as that for the whole data. It is experimentally verified that for test data, the quality of classification is comparable with that obtained using a nondecomposed data approach.

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Scalability of Data Decomposition Based Algorithms: Attribute Reduction Problem

Hońko

2015

Lecture Notes in Computer Science

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Attribute reduction: a horizontal data decomposition approach

Cited by 13 publications

References 21 publications

Breadth search strategies for finding minimal reducts: towards hardware implementation

Breadth search strategies for finding minimal reducts: towards hardware implementation

Binary classification rule generation from decomposed data

Scalability of Data Decomposition Based Algorithms: Attribute Reduction Problem

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