Shorter Rules Are Better, Aren’t They?

Stecher, Julius; Janssen, Frederik; Fürnkranz, Johannes

doi:10.1007/978-3-319-46307-0_18

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…Classification Rules: We used a simple top-down greedy hill-climbing algorithm that takes a seed example and generates a pair of rules, one with a regular heuristic (Laplace) and one with its inverted counterpart. As shown by Stecher et al (2016) and illustrated in Figure 5, this results in rule pairs that have approximately the same degree of generality but different complexities.…”

Section: Class Association Rulesmentioning

confidence: 93%

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On cognitive preferences and the plausibility of rule-based models

2019

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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 crowdsourcing 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.

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Section: Class Association Rulesmentioning

confidence: 93%

“…Thus, even though the shorter rules may be more comprehensible in the syntactic sense, the longer rules appear to be more plausible. Stecher et al (2016) and Valmarska et al (2017) investigated the suitability of such rules for subgroup discovery, with somewhat inconclusive results.…”

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confidence: 99%

On cognitive preferences and the plausibility of rule-based models

2019

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“…Current work in inductive rule learning is focused on finding simple rules via optimization (Dash et al, 2018;Wang et al, 2017;Malioutov and Meel, 2018), mostly with the goal that simple rules are more interpretable. However, there is also some evidence that shorter rules are not always more convincing than more complex rules (Fürnkranz et al, 2018;Stecher et al, 2016). Another line of research focuses on improving the accuracy of rule models, often by increasing their expressiveness through fuzzification, i.e., by making the decision boundary between different classes softer.…”

Section: Induction Of Predictive Rule Setsmentioning

confidence: 99%

Advances in Machine Learning for the Behavioral Sciences

Kliegr

Bahník

Fürnkranz

2019

American Behavioral Scientist

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The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces.

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“…Typically, it is understood as "given two explanations of the data, all other things being equal, the simpler explanation is preferable". However, there are a few rule learning algorithms that explicitly aim for longer rules, and it is not clear that shorter rules are indeed more comprehensible for human experts [97]. Other criteria, such as semantic coherence of the conditions of a rule, should thus be considered in the learning process [32].…”

Section: Interactive Model Interpretationmentioning

confidence: 99%

Interactive Data Analytics for the Humanities

Gurevych

Meyer

Binnig

et al. 2018

Computational Linguistics and Intelligent Text Processing

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In this vision paper, we argue that current solutions to data analytics are not suitable for complex tasks from the humanities, as they are agnostic of the user and focused on static, predefined tasks with large-scale benchmarks. Instead, we believe that the human must be put into the loop to address small data scenarios that require expert domain knowledge and fluid, incrementally defined tasks, which are common for many humanities use cases. Besides the main challenges, we discuss existing and urgently required solutions to interactive data acquisition, model development, model interpretation, and system support for interactive data analytics. In the envisioned interactive systems, human users not only provide annotations to a machine learner, but train a model by using the system and demonstrating the task. The learning system will actively query the user for feedback, refine its model in real-time, and is able to explain its decisions. Our vision links natural language processing research with recent advances in machine learning, computer vision, and data management systems, as realizing this vision relies on combining expertise from all of these scientific fields. 1 Challenges in Analyzing Humanities Data Automated data analytics, aka. data mining and machine learning, is a key technology for enriching and interpreting data, making informed decisions, and developing new data-driven scientific methods across many disciplines in industry and academia. Although the potential of interactive problem solving was recognized early on [16], this field has not progressed very far beyond the initial work. In particular, interactive machine learning and data analytics have only recently received increased attention [98]. Current data analytics solutions focus predominantly on well-defined tasks that can be solved by processing large, homogeneous datasets available in a structured form. Consider for example recommender systems, which suggest new products based on the product's properties, the products that the customer has previously bought, and the collective behavior of the customer database [43]. The state-of-the-art relies on huge amounts of data-over one billion pairs of users and news items passively gathered-to train a deep neural network [28]. This may explain, why data analytics is conceived in a rather impersonal way, with algorithms working autonomously on passively collected data, although practice is quite the opposite. Most of the influence practitioners have, comes through interacting with data, including crafting the data and examining results. In the late 1990s, digitized data became widely available in the humanities as well. Since then, there has been a clear demand for data analytics approaches to tap into these

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Shorter Rules Are Better, Aren’t They?

Cited by 10 publications

References 18 publications

On cognitive preferences and the plausibility of rule-based models

On cognitive preferences and the plausibility of rule-based models

Advances in Machine Learning for the Behavioral Sciences

Interactive Data Analytics for the Humanities

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