2018/11/17 by José-Ramón Cano, Cano, José-Ramón, Pedro Antonio Gutiérrez +7 · 2 citations
Business, Management and Accounting · Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.1811.07155
openalex publication_date 2018/11/17 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
Currently, knowledge discovery in databases is an essential step to identify\nvalid, novel and useful patterns for decision making. There are many real-world\nscenarios, such as bankruptcy prediction, option pricing or medical diagnosis,\nwhere the classification models to be learned need to fulfil restrictions of\nmonotonicity (i.e. the target class label should not decrease when input\nattributes values increase). For instance, it is rational to assume that a\nhigher debt ratio of a company should never result in a lower level of\nbankruptcy risk. Consequently, there is a growing interest from the data mining\nresearch community concerning monotonic predictive models. This paper aims to\npresent an overview about the literature in the field, analyzing existing\ntechniques and proposing a taxonomy of the algorithms based on the type of\nmodel generated. For each method, we review the quality metrics considered in\nthe evaluation and the different data sets and monotonic problems used in the\nanalysis. In this way, this paper serves as an overview of the research about\nmonotonic classification in specialized literature and can be used as a\nfunctional guide of the field.\n