9,475 research outputs found
Using rule extraction to improve the comprehensibility of predictive models.
Whereas newer machine learning techniques, like artifficial neural net-works and support vector machines, have shown superior performance in various benchmarking studies, the application of these techniques remains largely restricted to research environments. A more widespread adoption of these techniques is foiled by their lack of explanation capability which is required in some application areas, like medical diagnosis or credit scoring. To overcome this restriction, various algorithms have been proposed to extract a meaningful description of the underlying `blackbox' models. These algorithms' dual goal is to mimic the behavior of the black box as closely as possible while at the same time they have to ensure that the extracted description is maximally comprehensible. In this research report, we first develop a formal definition of`rule extraction and comment on the inherent trade-off between accuracy and comprehensibility. Afterwards, we develop a taxonomy by which rule extraction algorithms can be classiffied and discuss some criteria by which these algorithms can be evaluated. Finally, an in-depth review of the most important algorithms is given.This report is concluded by pointing out some general shortcomings of existing techniques and opportunities for future research.Models; Model; Algorithms; Criteria; Opportunities; Research; Learning; Neural networks; Networks; Performance; Benchmarking; Studies; Area; Credit; Credit scoring; Behavior; Time;
Improving Retrieval-Based Question Answering with Deep Inference Models
Question answering is one of the most important and difficult applications at
the border of information retrieval and natural language processing, especially
when we talk about complex science questions which require some form of
inference to determine the correct answer. In this paper, we present a two-step
method that combines information retrieval techniques optimized for question
answering with deep learning models for natural language inference in order to
tackle the multi-choice question answering in the science domain. For each
question-answer pair, we use standard retrieval-based models to find relevant
candidate contexts and decompose the main problem into two different
sub-problems. First, assign correctness scores for each candidate answer based
on the context using retrieval models from Lucene. Second, we use deep learning
architectures to compute if a candidate answer can be inferred from some
well-chosen context consisting of sentences retrieved from the knowledge base.
In the end, all these solvers are combined using a simple neural network to
predict the correct answer. This proposed two-step model outperforms the best
retrieval-based solver by over 3% in absolute accuracy.Comment: 8 pages, 2 figures, 8 tables, accepted at IJCNN 201
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Accuracy and interpretability trade-offs in machine learning applied to safer gambling
Responsible gambling is an area of research and industry which seeks to understand the pathways to harm from gambling and implement programmes to reduce or prevent harm that gambling might cause. There is a growing body of research that has used gambling behavioural data to model and predict harmful gambling, and the industry is showing increasing interest in technologies that can help gambling operators to better predict harm and prevent it through appropriate interventions. However, industry surveys and feedback clearly indicate that in order to enable wider adoption of such data-driven methods, industry and policy makers require a greater understanding of how machine learning methods make these predictions. In this paper, we make use of the TREPAN algorithm for extracting decision trees from Neural Networks and Random Forests. We present the first comparative evaluation of predictive performance and tree properties for extracted trees, which is also the first comparative evaluation of knowledge extraction for safer gambling. Results indicate that TREPAN extracts better performing trees than direct learning of decision trees from the data. Overall, trees extracted with TREPAN from different models offer a good compromise between prediction accuracy and interpretability. TREPAN can produce decision trees with extended tests rules of different forms, so that interpretability depends on multiple factors. We present detailed results and a discussion of the trade-offs with regard to performance and interpretability and use in the gambling industry
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
While this design allows to efficiently execute single model components at
training time, prediction serving has different requirements such as low
latency, high throughput and graceful performance degradation under heavy load.
Current prediction serving systems consider models as black boxes, whereby
prediction-time-specific optimizations are ignored in favor of ease of
deployment. In this paper, we present PRETZEL, a prediction serving system
introducing a novel white box architecture enabling both end-to-end and
multi-model optimizations. Using production-like model pipelines, our
experiments show that PRETZEL is able to introduce performance improvements
over different dimensions; compared to state-of-the-art approaches PRETZEL is
on average able to reduce 99th percentile latency by 5.5x while reducing memory
footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems
Design and Implementation (OSDI), 201
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