On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

43rd Annual Spring Concert at the University of Dayton

News release announcing the University of Dayton\u27s 43rd annual Spring Concert will be presented by the 65-member University of Dayton Concert Band and the University Choir

The cultural evolution of age-at-marriage norms

We present an agent-based model designed to study the cultural evolution of age-at-marriage norms. We review theoretical arguments and empirical evidence on the existence of norms proscribing marriage outside of an acceptable age interval. Using a definition of norms as constraints built in agents, we model the transmission of norms, and of mechanisms of intergenerational transmission of norms. Agents can marry each other only if they share part of the acceptable age interval. We perform several simulation experiments on the evolution across generations. In particular, we study the conditions under which norms persist in the long run, the impact of initial conditions, the role of random mutations, and the impact of social influence. Although the agent-based model we use is highly stylized, it gives important insights on the societal-level dynamics of life-course norms.

On Learning Vector Representations in Hierarchical Label Spaces

An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. This paper addresses one such problem, namely how to exploit hierarchical structures over labels. We present a novel method to learn vector representations of a label space given a hierarchy of labels and label co-occurrence patterns. Our experimental results demonstrate qualitatively that the proposed method is able to learn regularities among labels by exploiting a label hierarchy as well as label co-occurrences. It highlights the importance of the hierarchical information in order to obtain regularities which facilitate analogical reasoning over a label space. We also experimentally illustrate the dependency of the learned representations on the label hierarchy

Searching for patterns in political event sequences: Experiments with the KEDs database

This paper presents an empirical study on the possibility of discovering interesting event sequences and sequential rules in a large database of international political events. A data mining algorithm first presented by Mannila and Toivonen (1996), has been implemented and extended, which is able to search for generalized episodes in such event databases. Experiments conducted with this algorithm on the Kansas Event Data System (KEDS) database, an event data set covering interactions between countries in the Persian Gulf region, are described. Some qualitative and quantitative results are reported, and experiences with strategies for reducing the problem complexity and focusing on the search on interesting subsets of events are described

Large-scale Multi-label Text Classification - Revisiting Neural Networks

Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks. In particular, we show that BP-MLL's ranking loss minimization can be efficiently and effectively replaced with the commonly used cross entropy error function, and demonstrate that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting. Our experimental results show that simple NN models equipped with advanced techniques such as rectified linear units, dropout, and AdaGrad perform as well as or even outperform state-of-the-art approaches on six large-scale textual datasets with diverse characteristics.Comment: 16 pages, 4 figures, submitted to ECML 201