“Want to come play with me?” Outlier subgroup discovery on spatio‐temporal interactions

YuMi, Come and Play with Me! A Collaborative Robot for Piecing Together a Tangram Puzzle

You want me to do what?

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You Want Me to Predict What?

Geomorphic models have a wide range of capabilities in terms of prediction, and this range is part of the reason for this volume. Haff [1996] pointed out that landscape-scale geomorphic models are fundamentally ill-suited to precise prediction and that reductionism is ill-suited to landscapescale problems. Geomorphic models run the gamut of spatial and temporal scales and modeling approaches, f...

Subgroup Discovery with CN2-SD

This paper investigates how to adapt standard classification rule learning approaches to subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorit...

Model Reuse with Subgroup Discovery

In this paper we describe a method to reuse models with Model-Based Subgroup Discovery (MBSD), which is a extension of the Subgroup Discovery scheme. The task is to predict the number of bikes at a new rental station 3 hours in advance. Instead of training new models with the limited data from these new stations, our approach first selects a number of pre-trained models from old rental stations...