Performance Evaluation of Multi-instance Multi-label Classification using Kernel based K-Nearest Neighbour Algorithm
Authors:
- 1 November 2019
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1170-1175
Abstract
A new learning methodology Multi-instance Multi-label learning (MIML) developed to address the problems of identifying multiple instances belonging to many class labels. This framework is used to understand how multiple instances or complex objects with different semantic meanings are related to many labels. In existing approaches algorithms like Support Vector Machines, AdaBoost, K-Nearest Neighbor, are adapted for the MIML framework. In this paper, a varied approach of adapting Polynomial Kernel and Linear Kernel before applying K-Nearest Neighbour algorithm for MIML framework is proposed. The Kernel provides a better linear separation of the data in the projected space and removes noise. Hausdorff metric is used in K-Nearest Neighbour algorithm to compute the distance between the training sample and the test sample. On performance evaluation it can be seen that the integration of the Polynomial Kernel and Linear Kernel method with the K-Nearest Neighbour algorithm for MIML framework (MIML-KKNN) has better performance in terms of ranking loss, and coverage errors when compared to KNN without Kernel function (MIML-KNN).Keywords
This publication has 9 references indexed in Scilit:
- Metric Learning-Based Multi-Instance Multi-Label Classification With Label CorrelationIEEE Access, 2019
- Multi-Label Metric Transfer Learning Jointly Considering Instance Space and Label Space Distribution DivergenceIEEE Access, 2019
- A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm2010
- Label ranking by learning pairwise preferencesArtificial Intelligence, 2008
- ML-KNN: A lazy learning approach to multi-label learningPattern Recognition, 2007
- Learning multi-label scene classificationPattern Recognition, 2004
- Kernel Nearest-Neighbor AlgorithmNeural Processing Letters, 2002
- Machine learning of functional class from phenotype dataBioinformatics, 2002
- BoosTexter: A Boosting-based System for Text CategorizationMachine Learning, 2000