Artificial Intelligence

    

A Genetic Algorithm and Discriminant Analysis Based Outlier Detector

Authors: Eren Unlu

Fisher Discriminant Analysis (FDA), also known as Linear Discriminant Analysis (LDA) is a simple in nature yet highly effective tool for classification for vast types of datasets and settings. In this paper, we propose to leverage the discriminative potency of FDA for an unsupervised outlier detection algorithm. Unsupervised anomaly detection has been a topic of high interest in literature due to its numerous practical applications and fuzzy nature of subjective interpretation of success, therefore it is important to have different types of algorithms which can deliver distinct perspectives. Proposed method selects the subset of outlier points based on the maximization of LDA distance between the class of non-outliers via genetic algorithm.

Comments: 4 Pages.

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Submission history

[v1] 2020-10-19 19:41:58

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