Datum-Wise Classification: A Sequential Approach to Sparsity

Dulac-Arnold, Gabriel; Denoyer, Ludovic; Preux, Philippe; Gallinari, Patrick

doi:10.1007/978-3-642-23780-5_34

Cited by 30 publications

(29 citation statements)

References 10 publications

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“…This motivates the development of a framework in which a controller can select the rankers to be evaluated based on the characteristics of individual queries (Cambazoglu et al 2010). Our future goal here is to model the problem as a Markov decision process, and solve it using standard reinforcement learning techniques (Dulac-Arnold et al 2011;Benbouzid et al 2011Benbouzid et al , 2012a. For the formal description, let X = (x 1 , .…”

Section: Discussionmentioning

confidence: 99%

Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers

et al. 2013

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In subset ranking, the goal is to learn a ranking function that approximates a gold standard partial ordering of a set of objects (in our case, a set of documents retrieved for the same query). The partial ordering is given by relevance labels representing the relevance of documents with respect to the query on an absolute scale. Our approach consists of three simple steps. First, we train standard multi-class classifiers (AdaBoost.MH and multi-class SVM) to discriminate between the relevance labels. Second, the posteriors of multi-class classifiers are calibrated using probabilistic and regression losses in order to estimate the Bayes-scoring function which optimizes the Normalized Discounted Cumulative Gain (NDCG). In the third step, instead of selecting the best multi-class hyperparameters and the best calibration, we mix all the learned models in a simple ensemble scheme. Mach Learn (2013) 93:261-292 Our extensive experimental study is itself a substantial contribution. We compare most of the existing learning-to-rank techniques on all of the available large-scale benchmark data sets using a standardized implementation of the NDCG score. We show that our approach is competitive with conceptually more complex listwise and pairwise methods, and clearly outperforms them as the data size grows. As a technical contribution, we clarify some of the confusing results related to the ambiguities of the evaluation tools, and propose guidelines for future studies.

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Section: Discussionmentioning

confidence: 99%

Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers

et al. 2013

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“…We mathematically describe the Markov Decision Process (MDP) with similar notations of [9,17] as follows:…”

Section: Problem Definitionmentioning

confidence: 99%

Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment

Lee

Siewiorek

Smailagic

et al. 2020

Proceedings of the ACM Conference on Health, Inference, and Learning

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Automated assessment of rehabilitation exercises using machine learning has a potential to improve current rehabilitation practices. However, it is challenging to completely replicate therapist's decision making on the assessment of patients with various physical conditions. This paper describes an interactive machine learning approach that iteratively integrates a data-driven model with expert's knowledge to assess the quality of rehabilitation exercises. Among a large set of kinematic features of the exercise motions, our approach identifies the most salient features for assessment using reinforcement learning and generates a user-specific analysis to elicit feature relevance from a therapist for personalized rehabilitation assessment. While accommodating therapist's feedback on feature relevance, our approach can tune a generic assessment model into a personalized model. Specifically, our approach improves performance to predict assessment from 0.8279 to 0.9116 average F1-scores of three upper-limb rehabilitation exercises (< 0.01). Our work demonstrates that machine learning models with feature selection can generate kinematic feature-based analysis as explanations on predictions of a model to elicit expert's knowledge of assessment, and how machine learning models can augment with expert's knowledge for personalized rehabilitation assessment. CCS CONCEPTS • Human-centered computing → Interactive systems and tools; • Applied computing → Health care information systems; • Theory of computation → Sequential decision making.

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“…We mathematically describe the Markov Decision Process (MDP) with similar notations of [7,15] as follows:…”

Section: Problem Definitionmentioning

confidence: 99%

An Intelligent Decision Support System for Stroke Rehabilitation Assessment

Lee

2019

The 21st International ACM SIGACCESS Conference on Computers and Accessibility

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Rehabilitation assessment is critical to determine an adequate intervention for a patient. However, the current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspective on assessment with quantitative information on patient's exercise motions). As a result, we developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning to assess the quality of motion and summarize patient specific analysis. We evaluated this system with seven therapists using the dataset from 15 patient performing three exercises. The evaluation demonstrates that our system is preferred over a traditional system without analysis while presenting more useful information and significantly increasing the agreement over therapists' evaluation from 0.6600 to 0.7108 F1-scores (p < 0.05). We discuss the importance of presenting contextually relevant and salient information and adaptation to develop a human and machine collaborative decision making system.

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Datum-Wise Classification: A Sequential Approach to Sparsity

Cited by 30 publications

References 10 publications

Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers

Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers

Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment

An Intelligent Decision Support System for Stroke Rehabilitation Assessment

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