Paper Info
Title
Relevance as a metric for evaluating machine learning algorithms
Abstract
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this paper, we propose a novel probability-based performance metric called Relevance Score for evaluating supervised learning algorithms. We evaluate the proposed metric through empirical analysis on a dataset gathered from an intelligent lighting pilot installation. In comparison to the commonly used Classification Accuracy metric, the Relevance Score proves to be more appropriate for a certain class of applications.
Year
DOI
Venue
2013
10.1007/978-3-642-39712-7_15
machine learning and data mining in pattern recognition
Keywords
DocType
Volume
different algorithm,novel probability-based performance metric,application domain,classification accuracy,intelligent lighting pilot installation,empirical analysis,relevance score,machine learning,certain class
Conference
abs/1303.7093
Citations 
PageRank 
References 
5
0.55
11
Authors
4
Name
Order
Citations
PageRank
Aravind Kota Gopalakrishna1384.55
Tanir Ozcelebi214824.48
Antonio Liotta392.45
Johan J. Lukkien467170.50