Institute of Electrical & Electronic Engineers
Support-Vector Machines (SVM) has received a great interest in the machine learning community since their introduction, especially in Outlier Detection in Wireless Sensor Networks (WSN). The Quarter-Sphere formulation of One-Class SVM (QS-SVM), extends the main SVM ideas from supervised to unsupervised learning algorithms. The QS-SVM formulation is based only on Spatio-Temporal correlations between the sensor nodes (hence the name Spatio-Temporal Quarter-Sphere SVM, ST-QS-SVM). Thus, it has a non-ideal performance. This work presents a new One-Class Quarter-Sphere SVM formulation based on the novel concept of Attribute Correlations between the sensor nodes, hence the name, Spatio-Temporal-Attribute Quarter-sphere SVM (STA-QS-SVM) formulation.