Title :
APPrOVE: Application-oriented validation and evaluation of supervised learners
Author :
Lavesson, Niklas ; Davidsson, Paul
Author_Institution :
Sch. of Comput., Blekinge Inst. of Technol., Ronneby, Sweden
Abstract :
Learning algorithm evaluation is usually focused on classification performance. However, the characteristics and requirements of real-world applications vary greatly. Thus, for a particular application, some evaluation criteria are more important than others. In fact, multiple criteria need to be considered to capture application-specific trade-offs. Many multi-criteria methods can be used for the actual evaluation but the problems of selecting appropriate criteria and metrics as well as capturing the trade-offs still persist. This paper presents a framework for application-oriented validation and evaluation (APPrOVE). The framework includes four sequential steps that together address the aforementioned problems and its use in practice is demonstrated through a case study.
Keywords :
learning (artificial intelligence); program verification; software performance evaluation; application oriented validation and evaluation; application specific trade off; learning algorithm evaluation; supervised learner; Classification algorithms; Image databases; Image recognition; Stress; Supervised learning; Text categorization; classification; evaluation; supervised learning;
Conference_Titel :
Intelligent Systems (IS), 2010 5th IEEE International Conference
Conference_Location :
London
Print_ISBN :
978-1-4244-5163-0
Electronic_ISBN :
978-1-4244-5164-7
DOI :
10.1109/IS.2010.5548402