期刊论文详细信息
NEUROCOMPUTING 卷:341
Monotonic classification: An overview on algorithms, performance measures and data sets
Article
Cano, Jose-Ramon1  Antonio Gutierrez, Pedro2  Krawczyk, Bartosz3  Wozniak, Michal4  Garcia, Salvador5 
[1] Univ Jaen, EPS Linares, Dept Comp Sci, Ave Univ S-N, Jaen 23700, Spain
[2] Univ Cordoba, Dept Comp Sci & Numer Anal, Cordoba, Spain
[3] Virginia Commonwealth Univ, Dept Comp Sci, Richmond, VA 23284 USA
[4] Wroclaw Univ Technol, Dept Comp Sci, Wyb Wyspianskiego 27, PL-50370 Wroclaw, Poland
[5] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain
关键词: Monotonic classification;    Ordinal classification;    Taxonomy;    Software;    Performance metrics;    Monotonic data sets;   
DOI  :  10.1016/j.neucom.2019.02.024
来源: Elsevier
PDF
【 摘 要 】

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. (C) 2019 Elsevier B.V. All rights reserved.

【 授权许可】

Free   

【 预 览 】
附件列表
Files Size Format View
10_1016_j_neucom_2019_02_024.pdf 1558KB PDF download
  文献评价指标  
  下载次数:0次 浏览次数:0次