10,166 research outputs found
Subjectively Interesting Subgroup Discovery on Real-valued Targets
Deriving insights from high-dimensional data is one of the core problems in
data mining. The difficulty mainly stems from the fact that there are
exponentially many variable combinations to potentially consider, and there are
infinitely many if we consider weighted combinations, even for linear
combinations. Hence, an obvious question is whether we can automate the search
for interesting patterns and visualizations. In this paper, we consider the
setting where a user wants to learn as efficiently as possible about
real-valued attributes. For example, to understand the distribution of crime
rates in different geographic areas in terms of other (numerical, ordinal
and/or categorical) variables that describe the areas. We introduce a method to
find subgroups in the data that are maximally informative (in the formal
Information Theoretic sense) with respect to a single or set of real-valued
target attributes. The subgroup descriptions are in terms of a succinct set of
arbitrarily-typed other attributes. The approach is based on the Subjective
Interestingness framework FORSIED to enable the use of prior knowledge when
finding most informative non-redundant patterns, and hence the method also
supports iterative data mining.Comment: 12 pages, 10 figures, 2 tables, conference submissio
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which,
contrary to standard multiclass classification, an instance can be associated
with several class labels simultaneously. In this chapter, we advocate a
rule-based approach to multi-label classification. Rule learning algorithms are
often employed when one is not only interested in accurate predictions, but
also requires an interpretable theory that can be understood, analyzed, and
qualitatively evaluated by domain experts. Ideally, by revealing patterns and
regularities contained in the data, a rule-based theory yields new insights in
the application domain. Recently, several authors have started to investigate
how rule-based models can be used for modeling multi-label data. Discussing
this task in detail, we highlight some of the problems that make rule learning
considerably more challenging for MLC than for conventional classification.
While mainly focusing on our own previous work, we also provide a short
overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models
in Computer Vision and Machine Learning. The Springer Series on Challenges in
Machine Learning. Springer (2018). See
http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further
informatio
Exceptionally monotone models : the rank correlation model class for exceptional model mining
Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target attributes interact in an unusual way. One instance of such an investigated form of interaction is Pearson's correlation coefficient between two targets. EMM then finds subgroups with an exceptionally linear relation between the targets. In this paper, we enrich the EMM toolbox by developing the more general rank correlation model class. We find subgroups with an exceptionally monotone relation between the targets. Apart from catering for this richer set of relations, the rank correlation model class does not necessarily require the assumption of target normality, which is implicitly invoked in the Pearson's correlation model class. Furthermore, it is less sensitive to outliers
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
Big-Data-Driven Materials Science and its FAIR Data Infrastructure
This chapter addresses the forth paradigm of materials research -- big-data
driven materials science. Its concepts and state-of-the-art are described, and
its challenges and chances are discussed. For furthering the field, Open Data
and an all-embracing sharing, an efficient data infrastructure, and the rich
ecosystem of computer codes used in the community are of critical importance.
For shaping this forth paradigm and contributing to the development or
discovery of improved and novel materials, data must be what is now called FAIR
-- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets
the stage for advances of methods from artificial intelligence that operate on
large data sets to find trends and patterns that cannot be obtained from
individual calculations and not even directly from high-throughput studies.
Recent progress is reviewed and demonstrated, and the chapter is concluded by a
forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W.
Andreoni), Springer 2018/201
Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery
International audienceKnowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research
Discovering a taste for the unusual: exceptional models for preference mining
Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes exceptional' varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge.This research has received funding from the ECSEL Joint Undertaking, the framework programme for research and innovation Horizon 2020 (2014-2020) under Grant Agreement Number 662189-MANTIS-2014-1
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