In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. For example, suppose a population has an average response rate of 5%, but a certain model (or rule) has identified a segment with a response rate of 20%. Then that segment would have a lift of 4.0 (20%/5%).

While some studies comparing rule-based classifiers enumerate a parameter over several values, most use all default values, presumably due to the high computational cost of jointly tuning multiple parameters. We show that thorough, joint optimization of search parameters on individual datasets gives higher out-of-sample precision than fixed baselines. We test on 1,000 relatively large synthetic datasets with widely-varying properties. We optimize heuristic beam search with the m-estimate interestingness measure. We jointly tune m, the beam size, and the maximum rule length. The beam size controls the extent of search, where over-searching can find spurious rules. m controls the bias toward higher-frequency rules, with the optimal value depending on the amount of noise in the dataset. We assert that such hyper-parameters affecting the frequency bias and extent of search should be optimized simultaneously, since both directly affect the false-discovery rate. While our method based on grid search and cross-validation is computationally intensive, we show that it can be massively parallelized, with our GPU implementation providing up to 28x speedup over a comparable multi-threaded CPU implementation.

Hot on the heels of our Fall 2015 Release webinar including our Association Discovery (aka Association Rule Learning) implementation, we wanted to give this new capability a spin on our blog in order to get our readers warmed up.

This paper deals with the binary classification task when the target class has the lower probability of occurrence. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this short-coming of these methods which yield classifiers with low sensibility, we tackled the classification problem here through an approach based on the association rules learning. This approach has the advantage of allowing the identification of the patterns that are well correlated with the target class. Association rules learning is a well known method in the area of data-mining. It is used when dealing with large database for unsupervised discovery of local patterns that expresses hidden relationships between input variables. In considering association rules from a supervised learning point of view, a relevant set of weak classifiers is obtained from which one derives a classifier that performs well.

In this paper, we consider the problem of learning commonsenseknowledge in the form of first-order rules from incomplete and noisynatural-language extractions produced by an off-the-shelf informationextraction (IE) system. Much of the information conveyed in text mustbe inferred from what is explicitly stated since easily inferablefacts are rarely mentioned. The proposed rule learner accounts forthis phenomenon by learning rules in which the body of the rulecontains relations that are usually explicitly stated, while the heademploys a less-frequently mentioned relation that is easilyinferred. The rule learner processes training examples in an onlinemanner to allow it to scale to large text corpora. Furthermore, wepropose a novel approach to weighting rules using a curated lexicalontology like WordNet. The learned rules along with their parametersare then used to infer implicit information using a Bayesian LogicProgram. Experimental evaluation on a machine reading testbeddemonstrates the efficacy of the proposed methods.

Feedback on player experience and behaviour can be invaluable to game designers, but there is need for specialised knowledge discovery tools to deal with high volume playtest data. We describe a study witha commercial third-person shooter, in which integrated player activity and experience data was captured and mined for design-relevant knowledge. We demonstrate that association rule learning and rule templates can be used to extractmeaningful rules relating player activity and experience during combat. We found that the number, type and quality of rules varies between experiences, and is affected by feature distributions. Further work is required on rule selection and evaluation.