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 Multi-objective 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.
Cite
Text
Last. "Multi-Target Classification: Methodology and Practical Case Studies." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_35Markdown
[Last. "Multi-Target Classification: Methodology and Practical Case Studies." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/last2016ecmlpkdd-multitarget/) doi:10.1007/978-3-319-46131-1_35BibTeX
@inproceedings{last2016ecmlpkdd-multitarget,
title = {{Multi-Target Classification: Methodology and Practical Case Studies}},
author = {Last, Mark},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {280-283},
doi = {10.1007/978-3-319-46131-1_35},
url = {https://mlanthology.org/ecmlpkdd/2016/last2016ecmlpkdd-multitarget/}
}