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

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.

Integrative Windowing

In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.Comment: See http://www.jair.org/ for any accompanying file

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

How important are household demographic characteristics to explain private car use patterns? A multilevel approach to Austrian data

Private car use is one of the major contributors to pollution in industrialised countries. It is therefore important to understand the factors that determine the demand for car use. In explaining the variability in car use, it is important to take into account household demographic characteristics and local and regional differences in infrastructure, in addition to the economic variables commonly used in the prevailing literature on the topic. The appropriate tool to explain car ownership and car use is, therefore, a multilevel statistical approach. An Austrian household survey from 1997 finds that household characteristics such as age, gender, education and employment of the household head, household size and housing quality can effect the variability of car ownership and car use. The same survey also gives a clear indication of regional heterogeneity. This heterogeneity persists when we controlled for the variability of regional economic welfare and infrastructure as indicated by population density.

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

Pathways to stepfamily formation in Europe

Increasing proportions of couples are making childbearing decisions in stepfamilies but there has been no general comparative picture across European countries on stepfamily formation. The present paper aims to fill this gap and provides a comparison of European countries using macro-level indicators that describe union formation and dissolution and childbearing. We use the individual-level data files (standard recode files) of Fertility and Family Surveys from 19 European countries. Our results highlight the different pathways to a stepfamily in Europe, and show that in most European countries a considerable proportion of women form a stepfamily in childbearing ages, which needs to be considered in studies of fertility.childbearing histories, Europe, FFS, macro-level indicators, pre-union children, stepfamily, union histories

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