Stacked-MLkNN: A stacking based improvement to Multi-Label k-Nearest Neighbours

Files in This Item:
File Description SizeFormat 
pakrashi17a.pdf512.99 kBAdobe PDFDownload
Title: Stacked-MLkNN: A stacking based improvement to Multi-Label k-Nearest Neighbours
Authors: Pakrashi, Arjun
MacNamee, Brian
Permanent link:
Date: 22-Sep-2017
Online since: 2019-04-18T10:10:01Z
Abstract: Multi-label classification deals with problems where each datapoint can be assigned to more than one class, or label, at the same time. The simplest approach for such problems is to train independent binary classification models for each label and use these models to independently predict a set of relevant labels for a datapoint. MLkNN is an instance-based lazy learning algorithm for multi-label classification that takes this approach. MLkNN, and similar algorithms, however, do not exploit associations which may exist between the set of potential labels. These methods also suffer from imbalance in the frequency of labels in a training dataset. This work attempts to improve the predictions of MLkNN by implementing a two-layer stack-like method, Stacked-MLkNN which exploits the label associations. Experiments show that Stacked-MLkNN produces better predictions than MLkNN and several other state-of-the-art instance-based learning algorithms.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: JMLR
Copyright (published version): 2017 the Authors
Keywords: Multi-labelStackingInstance-based learning
Other versions:
Language: en
Status of Item: Not peer reviewed
Is part of: Proceedings of Machine Learning Research. Volume 74: First International Workshop on Learning with Imbalanced Domains: Theory and Applications, 22 September 2017, ECML-PKDD, Skopje, Macedonia
Conference Details: The 1st International Workshop on Learning with Imbalanced Domains: Theory and Applications (LIDTA 2017), Skopje, Macedonia, 18-22 September
Appears in Collections:Computer Science Research Collection

Show full item record

Google ScholarTM


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.