@inproceedings{1dee9f5439574cca89ac645727469011,
title = "Multi-target classification: Methodology and practical case studies",
abstract = "Most classification algorithms are aimed at predicting the value or values of a single target (class) attribute. However, some real-world classification tasks involve several targets that need to be predicted simultaneously. The Multiobjective Info-Fuzzy Network (M-IFN) algorithm builds an ordered (oblivious) decision-tree model for a multi-target classification task. After summarizing the principles and the properties of the M-IFN algorithm, this paper reviews three case studies of applying M-IFN to practical problems in industry and science.",
keywords = "Decision trees, Information theory, Multi-objective info-fuzzy networks, Multi-target classification",
author = "Mark Last",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 ; Conference date: 19-09-2016 Through 23-09-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-46131-1_35",
language = "English",
isbn = "9783319461304",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "280--283",
editor = "Bj{\"o}rn Bringmann and Elisa Fromont and Nikolaj Tatti and Volker Tresp and Pauli Miettinen and Bettina Berendt and Gemma Garriga",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings",
address = "Germany",
}