Abstract
Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.
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Shahid, N., Naqvi, I.H. & Qaisar, S.B. One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments. Artif Intell Rev 43, 515–563 (2015). https://doi.org/10.1007/s10462-013-9395-x
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DOI: https://doi.org/10.1007/s10462-013-9395-x