SSL-VFC4.5: An approach to adapt Very Fast C4.5 classification algorithm to deal with semi-supervised learning


The growing popularity of audio and video streaming, industry 4.0 and IoT (Internet of Things) technologies contribute to the fast augment of the generation of various types of data. Therefore, to analyze these data for decision-making, supervised machine learning techniques need to be fast while keeping a suitable predicting performance even in many real-life scenarios where labeled data are expensive and hard to be gotten. To overcome this problem, this work proposes an adaptation to the Very Fast C4.5 (VFC4.5) algorithm implementing on it a semi-supervised impurity metric presented in the literature. The results pointed out that this adaptation can slightly increase the accuracy of the VFC4.5 when the datasets have the presence of a very few amount of labeled instances, but it increases the training time, especially when the number of labeled instances in the datasets increase.

Palavras-chave: Machine learning, intrinsically semi-supervised classification, fast classification, impurity-based metric, top-down induction of decision trees


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NASS, Carlos Eduardo; ORTÍZ DÍAZ, Agustín Alejandro; BALDO, Fabiano. SSL-VFC4.5: An approach to adapt Very Fast C4.5 classification algorithm to deal with semi-supervised learning. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 13-24. ISSN 2763-8979. DOI: