Semisupervised learning in pattern recognition with concept drift

Mitrokhin, M. A.; Zaharov, S M; Yu., Mitrokhina N.

doi:10.1109/mwent47943.2020.9067486

Cited by 1 publication

(1 citation statement)

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“…As for unsupervised learning, the labeling does not exist, and the task is basically to find any relations between data items. There are also so-called semisupervised learning methods that combine both labeled and unlabeled records being considered [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Decision Rules Construction: Algorithm Based on EAV Model

Żabiński

Zielosko

2020

Entropy

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In the paper, an approach for decision rules construction is proposed. It is studied from the point of view of the supervised machine learning task, i.e., classification, and from the point of view of knowledge representation. Generated rules provide comparable classification results to the dynamic programming approach for optimization of decision rules relative to length or support. However, the proposed algorithm is based on transformation of decision table into entity–attribute–value (EAV) format. Additionally, standard deviation function for computation of averages’ values of attributes in particular decision classes was introduced. It allows to select from the whole set of attributes only these which provide the highest degree of information about the decision. Construction of decision rules is performed based on idea of partitioning of a decision table into corresponding subtables. In opposite to dynamic programming approach, not all attributes need to be taken into account but only these with the highest values of standard deviation per decision classes. Consequently, the proposed solution is more time efficient because of lower computational complexity. In the framework of experimental results, support and length of decision rules were computed and compared with the values of optimal rules. The classification error for data sets from UCI Machine Learning Repository was also obtained and compared with the ones for dynamic programming approach. Performed experiments show that constructed rules are not far from the optimal ones and classification results are comparable to these obtained in the framework of the dynamic programming extension.

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Section: Introductionmentioning

confidence: 99%