Title
Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study
Abstract
Graph-based semi-supervised learning (SSL) algorithms perform well on a variety of domains, such as digit recognition and text classification, when the data lie on a low-dimensional manifold. However, it is surprising that these methods have not been effectively applied on time series classification tasks. In this paper, we provide a comprehensive empirical comparison of state-of-the-art graph-based SSL algorithms with respect to graph construction and parameter selection. Specifically, we focus in this paper on the problem of time series transductive classification on imbalanced data sets. Through a comprehensive analysis using recently proposed empirical evaluation models, we confirm some of the hypotheses raised on previous work and show that some of them may not hold in the time series domain. From our results, we suggest the use of the Gaussian Fields and Harmonic Functions algorithm with the mutual k-nearest neighbors graph weighted by the RBF kernel, setting k = 20 on general tasks of time series transductive classification on imbalanced data sets.
Year
DOI
Venue
2014
10.1109/ICPR.2014.649
Pattern Recognition
Keywords
Field
DocType
Gaussian distribution,feature selection,graph theory,learning (artificial intelligence),mathematics computing,pattern classification,time series,Gaussian fields,SSL algorithms,graph construction,graph-based semisupervised learning,harmonic function algorithm,imbalanced data sets,k-nearest neighbor graph,parameter selection,time series transductive classification
Empirical comparison,Transduction (machine learning),Data mining,Harmonic function,Data set,Radial basis function kernel,Computer science,Artificial intelligence,Manifold,Pattern recognition,Gaussian,Problem of time,Machine learning
Conference
ISSN
Citations 
PageRank 
1051-4651
4
0.39
References 
Authors
11
3
Name
Order
Citations
PageRank
Celso André R. de Sousa140.39
Vinícius M. A. de Souza2336.14
Gustavo E. Batista3192892.83