A Hybrid Model for Detecting Anomalous Ozone Values

Abstract

One class Support Vector Machine (OCSVM) is remarkably an efficient semi-supervised learning method for classifying one class anomaly in the applications such as fault detection in hardware, document classification, novelty detection, etc. However, many studies showed that due to the presence of anomalies in the training data the boundary measured by OCSVM is biased towards anomalies and thereby results in to low classification accuracy. Classifying ozone measurements obtained from the environment is one such application where the dataset composes huge number of anomalies due to irregularities in the deployed sensors. To this end, this paper presents a technique to improve the anomaly classification accuracy of OCSVM using Deep Belief Networks (DBN). First, the data are pre-processed and then DBN is used for extracting linearly separable data. This outcome is then given to the OCSVM for classification of anomalous ozone measurements in the next step. It is observed from the simulation results that the proposed method shows better classification and achieved high accuracy of 92.71%.