Contact personalization using a score understanding method

Lemaire, Vincent; Féraud, Raphaël; Voisine, Nicolas

doi:10.1109/ijcnn.2008.4633863

Cited by 21 publications

(10 citation statements)

References 8 publications

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“…Finally we note that the sensitivity portion of our measure (i.e., entropy aspect aside) differs from how other authors compute sensitivity globally across both instances and features [27].…”

Section: A New Measure: Simplicity Of Output Sensitivitymentioning

confidence: 82%

Measures of Model Interpretability for Model Selection

Carrington¹,

Fieguth²,

Chen³

2018

Lecture Notes in Computer Science

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The literature lacks definitions for quantitative measures of model interpretability for automatic model selection to achieve high accuracy and interpretability, hence we define inherent model interpretability. We extend the work of Lipton et al. and Liu et al. from qualitative and subjective concepts of model interpretability to objective criteria and quantitative measures. We also develop another new measure called simplicity of sensitivity and illustrate prior, initial and posterior measurement. Measures are tested and validated with some measures recommended for use. It is demonstrated that high accuracy and high interpretability are jointly achievable with little to no sacrifice in either.

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Section: A New Measure: Simplicity Of Output Sensitivitymentioning

confidence: 82%

Measures of Model Interpretability for Model Selection

Carrington¹,

Fieguth²,

Chen³

2018

Lecture Notes in Computer Science

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“…Contact personalization using a score understanding method 35 Computes the influence of a feature by measuring the effect of changing the feature's value on the model's prediction.…”

Section: Lemaire Et Al (2008)mentioning

confidence: 99%

Patient-Specific Explanations for Predictions of Clinical Outcomes

et al. 2019

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Background Machine learning models that are used for predicting clinical outcomes can be made more useful by augmenting predictions with simple and reliable patient-specific explanations for each prediction. Objectives This article evaluates the quality of explanations of predictions using physician reviewers. The predictions are obtained from a machine learning model that is developed to predict dire outcomes (severe complications including death) in patients with community acquired pneumonia (CAP). Methods Using a dataset of patients diagnosed with CAP, we developed a predictive model to predict dire outcomes. On a set of 40 patients, who were predicted to be either at very high risk or at very low risk of developing a dire outcome, we applied an explanation method to generate patient-specific explanations. Three physician reviewers independently evaluated each explanatory feature in the context of the patient's data and were instructed to disagree with a feature if they did not agree with the magnitude of support, the direction of support (supportive versus contradictory), or both. Results The model used for generating predictions achieved a F1 score of 0.43 and area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval [CI]: 0.81–0.87). Interreviewer agreement between two reviewers was strong (Cohen's kappa coefficient = 0.87) and fair to moderate between the third reviewer and others (Cohen's kappa coefficient = 0.49 and 0.33). Agreement rates between reviewers and generated explanations—defined as the proportion of explanatory features with which majority of reviewers agreed—were 0.78 for actual explanations and 0.52 for fabricated explanations, and the difference between the two agreement rates was statistically significant (Chi-square = 19.76, p-value < 0.01). Conclusion There was good agreement among physician reviewers on patient-specific explanations that were generated to augment predictions of clinical outcomes. Such explanations can be useful in interpreting predictions of clinical outcomes.

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“…In contrast, the advantage of model-independent methods is that they can be applied to an arbitrary predictive model. Several such approaches, which observe the sensitivity of the model by changing the values of attributes, exist (Lemaire et al, 2008;Robnik-Šikonja and Kononenko, 2008); however, they cannot detect interactions between attributes, for example their disjunctive dependencies, which results in less informative explanations. The method of has overcome this drawback.…”

Section: Explanation Of Predictive Models and Individual Predictionsmentioning

confidence: 99%

Enhancing data stream predictions with reliability estimators and explanation

Bosnić

Demšar

Kešpret

et al. 2014

Engineering Applications of Artificial Intelligence

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Incremental learning from data streams is increasingly attracting research focus due to many real streaming problems (such as learning from transactions, sensors or other sequential observations) that require processing and forecasting in the real time. In this paper we deal with two issues related to incremental learningprediction accuracy and prediction explanationand demonstrate their applicability on several streaming problems for predicting electricity load in the future. For improving prediction accuracy we propose and evaluate the use of two reliability estimators that allow us to estimate prediction error and correct predictions. For improving interpretability of the incremental model and its predictions we propose an adaptation of the existing prediction explanation methodology, which was originally developed for batch learning from stationary data. The explanation methodology is combined with a state-of-the-art concept drift detector and a visualization technique to enhance the explanation in dynamic streaming settings. The results show that the proposed approaches can improve prediction accuracy and allow transparent insight into the modeled concept.

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Contact personalization using a score understanding method

Cited by 21 publications

References 8 publications

Measures of Model Interpretability for Model Selection

Measures of Model Interpretability for Model Selection

Patient-Specific Explanations for Predictions of Clinical Outcomes

Enhancing data stream predictions with reliability estimators and explanation

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