An approach for outlier and novelty detection for text data based on classifier confidence

Article type: Research Article

Authors: Pižurica, Nikola | Tomović, Savo^{; *}

Affiliations: Faculty of Mathematics and Natural Sciences, University of Montenegro, Cetinjska 2, 81000 Podgorica, Montenegro. E-mails: [email protected], [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: In this paper we present an approach for novelty detection in text data. The approach can also be considered as semi-supervised anomaly detection because it operates with the training dataset containing labelled instances for the known classes only. During the training phase the classification model is learned. It is assumed that at least two known classes exist in the available training dataset. In the testing phase instances are classified as normal or anomalous based on the classifier confidence. In other words, if the classifier cannot assign any of the known class labels to the given instance with sufficiently high confidence (probability), the instance will be declared as novelty (anomaly). We propose two procedures to objectively measure the classifier confidence. Experimental results show that the proposed approach is comparable to methods known in the literature.

Keywords: Classification, novelty detection, outlier detection, classifier confidence, information retrieval

DOI: 10.3233/AIC-200649

Journal: AI Communications, vol. 33, no. 3-6, pp. 139-153, 2020