Interpretable Feature Recommendation for Signal Analytics
This paper presents an automated approach for interpretable feature recommendation for solving signal data analytics problems. The method has been tested by performing experiments on datasets in the domain of prognostics where interpretation of features is considered very important. The proposed approach is based on Wide Learning architecture and provides means for interpretation of the recommended features. It is to be noted that such an interpretation is not available with feature learning approaches like Deep Learning (such as Convolutional Neural Network) or feature transformation approaches like Principal Component Analysis. Results show that the feature recommendation and interpretation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in time to develop a solution. It is further shown by an example, how this human-in-loop interpretation system can be used as a prescriptive system.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Unified and Fast Interpretable Model for Predictive Analytics
Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus the machine learning model is preferred t…
Decision MakingVisually-Aware Personalized Recommendation using Interpretable Image Representations
Visually-aware recommender systems use visual signals present in the underlying data to model the visual characteristics of items and users' preferences towards them. In the domain of clothing recommendation, incorporati…
Interactive RecommendationRecommendation SystemsTRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications
In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions. Traditional machine learning mo…
Feature ImportanceManagementTime SeriesTime Series AnalysisautoNLP: NLP Feature Recommendations for Text Analytics Applications
While designing machine learning based text analytics applications, often, NLP data scientists manually determine which NLP features to use based upon their knowledge and experience with related problems. This results in…
BIG-bench Machine LearningFeature EngineeringMaking Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
Explaining recommendations enables users to understand whether recommended items are relevant to their needs and has been shown to increase their trust in the system. More generally, if designing explainable machine lear…
Interpretable Machine LearningMixture-of-Experts