Generalized mean based back-propagation of errors for ambiguity resolution

Citation data:

Pattern Recognition Letters, ISSN: 0167-8655, Vol: 94, Page: 22-29

Publication Year:
2017
Usage 5
Abstract Views 5
Social Media 120
Shares, Likes & Comments 120
DOI:
10.1016/j.patrec.2017.04.019
Author(s):
Shounak Datta, Sankha Subhra Mullick, Swagatam Das
Publisher(s):
Elsevier BV
Tags:
Computer Science
article description
Ambiguity in a dataset, characterized by data points having multiple target labels, may occur in many supervised learning applications. Such ambiguity originates naturally or from misinterpretation, faulty encoding, and/or incompleteness of data. However, most applications demand that a data point be assigned a single label. In such cases, the supervised learner must resolve the ambiguity. To effectively perform ambiguity resolution, we propose a new variant of the popular Multi-Layer Perceptron model, called the Generalized Mean Multi-Layer Perceptron (GMMLP). In GMMLP, a novel differentiable error function guides the back-propagation algorithm towards the minimum distant target for each data point. We evaluate the performance of the proposed algorithm against three alternative ambiguity resolvers on 20 new artificial datasets containing ambiguous data points. To further test for scalability and comparison with multi-label classifiers, 18 real datasets are also used to evaluate the new approach.

This article has 0 Wikipedia reference.