Monotonic classification: An overview on algorithms, performance measures and data sets

Áreas de investigación:

• Methodology - Ordinal classification

Año:

2019

Tipo de publicación:

Artículo

Palabras clave:

Monotonic classification, Ordinal classification, Taxonomy, Software, Performance metrics, Monotonic data sets

Autores:

• Cano, José-Ramón
• Gutiérrez, Pedro Antonio
• Krawczyk, Bartosz
• Wozniak, Michal
• García, Salvador

Journal:

Neurocomputing

Volumen:

341

Páginas:

168-182

Mes:

Mayo

ISSN:

0925-2312

BibTex:

@article{MonotonicReview2019, author = "Jos{\'e}-Ram{\'o}n Cano and Pedro Antonio Guti{\'e}rrez and Bartosz Krawczyk and Michal Wozniak and Salvador Garc{\'i}a", abstract = "Currently, knowledge discovery in databases is an essential first step when identifying valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview of the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of monotonic classification research in specialized literature and can be used as a functional guide for the field.", awards = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1016/j.neucom.2019.02.024", issn = "0925-2312", journal = "Neurocomputing", keywords = "Monotonic classification, Ordinal classification, Taxonomy, Software, Performance metrics, Monotonic data sets", month = "May", note = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", pages = "168-182", title = "{M}onotonic classification: {A}n overview on algorithms, performance measures and data sets", url = "doi.org/10.1016/j.neucom.2019.02.024", volume = "341", year = "2019", }

Nota:

JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.

Abstract:

Currently, knowledge discovery in databases is an essential first step when identifying valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview of the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of monotonic classification research in specialized literature and can be used as a functional guide for the field.

Comentarios:

JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.