TY - BOOK
T1 - Relevance as a metric for evaluating machine learning algorithms
AU - Kota Gopalakrishna, A.
AU - Ozcelebi, T.
AU - Liotta, A.
AU - Lukkien, J.J.
PY - 2013
Y1 - 2013
N2 - 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 work, 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.
Keywords: Machine learning algorithms; performance metric; probabilistic approach
AB - 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 work, 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.
Keywords: Machine learning algorithms; performance metric; probabilistic approach
M3 - Report
T3 - Computer science reports
BT - Relevance as a metric for evaluating machine learning algorithms
PB - Technische Universiteit Eindhoven
CY - Eindhoven
ER -