A framework for multidimensional learning using multilabel ranking
Shuaib Ahmed, B.P.C. Rao, T V Jayakumar · International Journal of Advanced Intelligence Paradigms · 2013
Multidimensional learning problem which is a more general form of supervised machine learning dealing with learning a function that maps a vector of input features to a vector of class labels is discussed in this paper. The study focuses on a framework which uses multilabel ranking with an emphasis on solving multidimensional learning problems. Performance of the framework is evaluated using mean accuracy, global accuracy and entropy of accuracy. Studies using real world benchmark dataset have shown that all the assumptions made in building the framework are valid. In addition to this, studies are carried out on images from eddy current non-destructive testing of stainless steel plate having subsurface defects clearly confirm that the proposed framework is well suitable for learning multidimensional problems and is superior to uni-dimensional learning algorithms.