Given a set of decision objects, an ordinal classifier is an algorithm that can group these objects into preference-ordered decision classes. A ranker is an algorithm that can sort a set of decision objects from highest to lowest, typically using a scoring function. In this paper we propose a ranker to transform the output of ordinal classifier into a ranking using elementary preference information extracted from the dataset. The proposed approach is illustrated using green car data in Taiwan.
|Title of host publication||Proceedings of the 26th IEEE International Conference on Automation and Computing (ICAC'21), Portsmouth, UK, 2-4 September, 2021|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Accepted for publication - 6 Jun 2021|
|Event||26th IEEE International Conference on Automation and Computing (ICAC'21) - Portsmouth, United Kingdom|
Duration: 2 Sep 2021 → 4 Sep 2021
|Conference||26th IEEE International Conference on Automation and Computing (ICAC'21)|
|Period||2/09/21 → 4/09/21|