Exceptional Model Mining

BoostEMM - Transparent Boosting using Exceptional Model Mining

Boosting is an iterative ensemble-learning paradigm. Every iteration, a weak predictor learns a classification task, taking into account performance achieved in previous iterations. This is done by assigning weights to individual records of the dataset, which are increased if the record is misclassified by the previous weak predictor. Hence, subsequent predictors learn to focus on problematic r...

A Rank Correlation Model Class for Exceptional Model Mining

Exceptional Preferences Mining

Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We...

Exceptional Model Mining with Tree-Constrained Gradient Ascent

Exceptional Model Mining (EMM) generalizes the wellknown data mining task of Subgroup Discovery (SD). Given a model class of interest, the goal of EMM is to find subgroups of the data for which a model fitted to the subgroup deviates substantially from the global model. In both SD and EMM, heuristic search is often employed to circumvent the problem that exhaustive search of the subgroup descri...

Generic Pattern Trees for Exhaustive Exceptional Model Mining

Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation...