3 research outputs found
Causality-based cost-effective action mining
In many business contexts, the ultimate goal of knowledge discovery is not the knowledge itself, but putting it to use. Models or patterns found by data mining methods often require further post-processing to bring this about. For instance, in churn prediction, data mining may give a model that predicts which customers are likely to end their contract, but companies are not just interested in knowing who is likely to do so, they want to know what they can do to avoid this. The models or patterns have to be transformed into actionable knowledge. Action mining explicitly addresses this. Currently, many action mining methods rely on a predictive model, obtained through data mining, to estimate the effect of certain actions and finally suggest actions with desirable effects. A major problem with this approach is that predictive models do not necessarily reflect a causal relationship between their inputs and outputs. This makes the existing action mining methods less reliable. In this paper, we introduce ICE-CREAM, a novel approach to action mining that explicitly relies on an automatically obtained best estimate of the causal relationships in the data. Experiments confirm that ICE-CREAM performs much better than the current state of the art in action mining
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From Classification Rules to Action Recommendations
Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not straightforward especially when the number of rules is large. Ideally, the user would ultimately like to use these rules to decide which actions to take. In the literature, this notion is usually referred to as actionability. The contribution of this paper1 is two-fold: first we propose a survey of the main approaches developed to address actionability. This topic has received growing attention in the past years. We present a classification of the main research in this area as well as a comparative study between the different approaches. Second, we propose a new framework to address actionability. Our goal is to lighten the burden of analyzing a large set of classification rules when the user is confronted with an "unsatisfactory situation" and needs help to decide what appropriate actions to take in order to remedy the situation. The method consists in comparing the situation to a set of classification rules. This is achieved by using a suitable distance that allows one to suggest action recommendations requiring minimal changes to improve the situation. We propose the algorithm DAKAR for learning action recommendations and we present an application to environment protection. Our experiment shows the usefulness of our contribution for action recommendation but also raises some concerns about the impact of the redundancy of a set of rules in learning action recommendations of good quality