Outlier detection using flexible categorisation and interrogative agendas

TL;DR

This paper introduces a novel FCA-based framework for outlier detection that incorporates flexible, agenda-driven feature categorization and combines unsupervised and supervised meta-learning approaches, providing explainability.

Contribution

It presents a new FCA-based outlier detection method using flexible feature agendas and a meta-learning approach to optimize these agendas for improved detection.

Findings

Algorithms perform comparably to standard outlier detection methods.

Provides both local and global explanations of outlier detection results.

Integrates subjective feature selection through interrogative agendas.

Abstract

Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task at hand. In their turn, the (a priori) choice of a particular set of features over another might be subjective and express a certain epistemic stance (e.g. interests, relevance, preferences) of an agent or a group of agents, namely, their interrogative agenda. In the present paper, we represent interrogative agendas as sets of features, and explore and compare different ways to categorize objects w.r.t. different sets of features (agendas). We first develop a simple unsupervised FCA-based algorithm for outlier detection which uses categorizations arising from different agendas. We then present a supervised meta-learning algorithm to learn suitable (fuzzy) agendas for categorization as sets of features with different weights or masses. We combine this meta-learning algorithm with the unsupervised outlier detection algorithm to obtain a supervised outlier detection algorithm. We show that these algorithms perform at par with commonly used algorithms for outlier detection on commonly used datasets in outlier detection. These algorithms provide both local and global explanations of their results.