Paper Info
Title
Multilayer one-class extreme learning machine.
Abstract
One-class classification has been found attractive in many applications for its effectiveness in anomaly or outlier detection. Representative one-class classification algorithms include the one-class support vector machine (SVM), Naive Parzen density estimation, autoencoder (AE), etc. Recently, the one-class extreme learning machine (OC-ELM) has been developed for learning acceleration and performance enhancement. But existing one-class algorithms are generally less effective in complex and multi-class classifications. To alleviate the deficiency, a multilayer neural network based one-class classification with ELM (in short, as ML-OCELM) is developed in this paper. The stacked AEs are employed in ML-OCELM to exploit an effective feature representation for complex data. The effective kernel based learning framework is also investigated in the stacked AEs of ML-OCELM, leading to a multilayer kernel based OC-ELM (in short, as MK-OCELM). The MK-OCELM has advantages of less human-intervention parameters and good generalization performance. Experiments on 13 benchmark UCI classification datasets and a real application on urban acoustic classification (UAC) are carried out to show the superiority of the proposed ML-OCELM/MK-OCELM over the OC-ELM and several state-of-the-art algorithms.
Year
DOI
Venue
2019
10.1016/j.neunet.2019.03.004
Neural Networks
Keywords
Field
DocType
One-class classification,OC-ELM,ML-OCELM,Kernel learning,Outlier/anomaly detection
Density estimation,Kernel (linear algebra),Anomaly detection,Autoencoder,Extreme learning machine,Support vector machine,Artificial intelligence,Artificial neural network,Statistical classification,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
115
1
0893-6080
Citations 
PageRank 
References 
5
0.42
0
Authors
5
Name
Order
Citations
PageRank
Haozhen Dai150.42
Jiuwen Cao217818.99
Tianlei Wang3349.77
Muqing Deng4122.53
Zhixin Yang511823.12