IMCOD: Incremental multi-class outlier detection model in data streams

Abstract

Multi-class outlier detection from multi-class data streams is a crucial yet challenging task for many real-life applications and services. Various detection methods are available e.g., derivation from one-class classification algorithms, prior knowledge of the underlying data distribution, method-specific hyperparameters tuning for different data sets. However, almost all of these methods are designed to work in the batch learning mode. Therefore, it is difficult to apply the existing methods to multi-class data stream applications. To overcome these limitations, this study proposes a new incremental multi-class outlier detection model (iMCOD). Unlike previous approaches, iMCOD incorporates an incremental support vector machine (iSVM) with an incremental local outlier factor (iLOF) in a unified framework. The incoming query sample was classified using iSVM, and the iLOF score was computed using the newly developed class-based nearest neighbor (NN) concept. Employing the class-based median of the NN average deviation metric, we determined whether the query sample is an outlier, and the iSVM was updated accordingly for the next sample. The iMCOD can detect outliers from multi-class data streams without the need for hyperparameter tuning. Extensive experimental and statistical analyses on 15 real-world data sets demonstrated that iMCOD outperformed 13 competitors.