CLUSplus: A decision tree-based framework for predicting structured outputs

Petković, Matej; Levatić, Jurica; Kocev, Dragi; Breskvar, Martin; Džeroski, Sašo

doi:10.1016/j.softx.2023.101526

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…Te PCT framework (PCTs are implemented in the CLUS system [21] available for download at https://github. com/knowledge-technologies/clus) treats a decision tree as a hierarchically organized set of clusters, where the topmost cluster contains all the data.…”

Section: Background: Predictive Clustering Treesmentioning

confidence: 99%

Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification

Levatić,

Ceci,

Kocev

et al. 2024

International Journal of Intelligent Systems

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Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled, but also unlabeled examples. While SSL for the simple tasks of classification and regression has received much attention from the research community, this is not the case for complex prediction tasks with structurally dependent variables, such as multi-label classification and hierarchical multi-label classification. These tasks may require additional information, possibly coming from the underlying distribution in the descriptive space provided by unlabeled examples, to better face the challenging task of simultaneously predicting multiple class labels. In this paper, we investigate this aspect and propose a (hierarchical) multi-label classification method based on semi-supervised learning of predictive clustering trees, which we also extend towards ensemble learning. Extensive experimental evaluation conducted on 24 datasets shows significant advantages of the proposed method and its extension with respect to their supervised counterparts. Moreover, the method preserves interpretability of classical tree-based models.

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Section: Background: Predictive Clustering Treesmentioning

confidence: 99%

Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification

Levatić,

Ceci,

Kocev

et al. 2024

International Journal of Intelligent Systems

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“…In our analysis, we declared specific fungi as labels, and we trained ML models to predict their presence in the water and on materials in contact with water. The PCT was generated using the CLUSPlus software package [29].…”

Section: Relating Environmental Factors To the Presence Of Fungi Isol...mentioning

confidence: 99%

Effect of Location, Disinfection, and Building Materials on the Presence and Richness of Culturable Mycobiota through Oligotrophic Drinking Water Systems

Novak Babič,

Marolt,

Imperl

et al. 2023

JoF

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Safe drinking water is a constant challenge due to global environmental changes and the rise of emerging pathogens—lately, these also include fungi. The fungal presence in water greatly varies between sampling locations. Little is known about fungi from water in combination with a selection of materials used in water distribution systems. Our research was focused on five water plants located in the Pannonian Plain, Slovenia. Sampled water originated from different natural water sources and was subjected to different cleaning methods before distribution. The average numbers of fungi from natural water, water after disinfection, water at the first sampling point in the water network, and water at the last sampling point were 260, 49, 64, and 97 CFU/L, respectively. Chlorination reduced the number of fungi by a factor of 5, but its effect decreased with the length of the water network. The occurrence of different fungi in water and on materials depended on the choice of material. The presence of the genera Aspergillus, Acremonium, Furcasterigmium, Gliomastix, and Sarocladium was mostly observed on cement, while Cadophora, Cladosporium, Cyphellophora, and Exophiala prevailed on metals. Plastic materials were more susceptible to colonization with basidiomycetous fungi. Opportunistically pathogenic fungi were isolated sporadically from materials and water and do not represent a significant health risk for water consumers. In addition to cultivation data, physico-chemical features of water were measured and later processed with machine learning methods, revealing the sampling location and water cleaning processes as the main factors affecting fungal presence and richness in water and materials in contact with water.

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Semi-Supervised Multi-Label Classification of Land Use/Land Cover in Remote Sensing Images With Predictive Clustering Trees and Ensembles

Stoimchev,

Levatić,

Kocev

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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CLUSplus: A decision tree-based framework for predicting structured outputs

Cited by 4 publications

References 52 publications

Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification

Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification

Effect of Location, Disinfection, and Building Materials on the Presence and Richness of Culturable Mycobiota through Oligotrophic Drinking Water Systems

Semi-Supervised Multi-Label Classification of Land Use/Land Cover in Remote Sensing Images With Predictive Clustering Trees and Ensembles

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