Bayesian Classifier Fusion with an Explicit Model of Correlation

Combining the outputs of multiple classifiers or experts into a single probabilistic classification is a fundamental task in machine learning with broad applications from classifier fusion to expert opinion pooling. Here we present a hierarchical Bayesian model of probabilistic classifier fusion based on a new correlated Dirichlet distribution. This distribution explicitly models positive correlations between marginally Dirichlet-distributed random vectors thereby allowing explicit modeling of correlations between base classifiers or experts. The proposed model naturally accommodates the classic Independent Opinion Pool and other independent fusion algorithms as special cases. It is evaluated by uncertainty reduction and correctness of fusion on synthetic and real-world data sets. We show that a change in performance of the fused classifier due to uncertainty reduction can be Bayes optimal even for highly correlated base classifiers.Comment: 12 pages, 4 figures, 1 table, revised title and Fig 2, added real data set Bookies

Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction

Assistive robots can potentially improve the quality of life and personal independence of elderly people by supporting everyday life activities. To guarantee a safe and intuitive interaction between human and robot, human intentions need to be recognized automatically. As humans communicate their intentions multimodally, the use of multiple modalities for intention recognition may not just increase the robustness against failure of individual modalities but especially reduce the uncertainty about the intention to be predicted. This is desirable as particularly in direct interaction between robots and potentially vulnerable humans a minimal uncertainty about the situation as well as knowledge about this actual uncertainty is necessary. Thus, in contrast to existing methods, in this work a new approach for multimodal intention recognition is introduced that focuses on uncertainty reduction through classifier fusion. For the four considered modalities speech, gestures, gaze directions and scene objects individual intention classifiers are trained, all of which output a probability distribution over all possible intentions. By combining these output distributions using the Bayesian method Independent Opinion Pool the uncertainty about the intention to be recognized can be decreased. The approach is evaluated in a collaborative human-robot interaction task with a 7-DoF robot arm. The results show that fused classifiers which combine multiple modalities outperform the respective individual base classifiers with respect to increased accuracy, robustness, and reduced uncertainty.Comment: Submitted to IROS 201

Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction

Assistive robots can potentially improve the quality of life and personal independence of elderly people by supporting everyday life activities. To guarantee a safe and intuitive interaction between human and robot, human intentions need to be recognized automatically. As humans communicate their intentions multimodally, the use of multiple modalities for intention recognition may not just increase the robustness against failure of individual modalities but especially reduce the uncertainty about the intention to be recognized. This is desirable as particularly in direct interaction between robots and potentially vulnerable humans a minimal uncertainty about the situation as well as knowledge about this actual uncertainty is necessary. Thus, in contrast to existing methods, in this work a new approach for multimodal intention recognition is introduced that focuses on uncertainty reduction through classifier fusion. For the four considered modalities speech, gestures, gaze directions and scene objects individual intention classifiers are trained, all of which output a probability distribution over all possible intentions. By combining these output distributions using the Bayesian method Independent Opinion Pool [1] the uncertainty about the intention to be recognized can be decreased. The approach is evaluated in a collaborative human-robot interaction task with a 7-DoF robot arm. The results show that fused classifiers, which combine multiple modalities, outperform the respective individual base classifiers with respect to increased accuracy, robustness, and reduced uncertainty

Bayesian Classifier Fusion with an Explicit Model of Correlation

Combining the outputs of multiple classifiers or experts into a single probabilistic classification is a fundamental task in machine learning with broad applications from classifier fusion to expert opinion pooling. Here we present a hierarchical Bayesian model of probabilistic classifier fusion based on a new correlated Dirichlet distribution. This distribution explicitly models positive correlations between marginally Dirichlet-distributed random vectors thereby allowing explicit modeling of correlations between base classifiers or experts. The proposed model naturally accommodates the classic Independent Opinion Pool and other independent fusion algorithms as special cases. It is evaluated by uncertainty reduction and correctness of fusion on synthetic and real-world data sets. We show that a change in performance of the fused classifier due to uncertainty reduction can be Bayes optimal even for highly correlated base classifiers

A normative model for Bayesian combination of subjective probability estimates

Combining experts’ subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally. Since the beta distribution is a common choice for modeling uncertainty about probabilities, here we propose a family of normative Bayesian models for aggregating probability estimates based on beta distributions. We systematically derive and compare different variants, including hierarchical and non-hierarchical as well as asymmetric and symmetric beta fusion models. Using these models, we show how the beta calibration function naturally arises in this normative framework and how it is related to the widely used Linear-in-Log-Odds calibration function. For evaluation, we provide the new Knowledge Test Confidence data set consisting of subjective probability estimates of 85 forecasters on 180 queries. On this and another data set, we show that the hierarchical symmetric beta fusion model performs best of all beta fusion models and outperforms related Bayesian fusion models in terms of mean absolute error

Diagnostic Issues, Clinical Characteristics, and Outcomes for Patients with Fungemia▿

This study investigated microbiological, clinical, and management issues and outcomes for Danish fungemia patients. Isolates and clinical information were collected at six centers. A total of 334 isolates, 316 episodes, and 305 patients were included, corresponding to 2/3 of the national episodes. Blood culture positivity varied by system, species, and procedure. Thus, cases with concomitant bacteremia were reported less commonly by BacT/Alert than by the Bactec system (9% [11/124 cases] versus 28% [53/192 cases]; P < 0.0001), and cultures with Candida glabrata or those drawn via arterial lines needed longer incubation. Species distribution varied by age, prior antifungal treatment (57% occurrence of C. glabrata, Saccharomyces cerevisiae, or C. krusei in patients with prior antifungal treatment versus 28% occurrence in those without it; P = 0.007), and clinical specialty (61% occurrence of C. glabrata or C. krusei in hematology wards versus 27% occurrence in other wards; P = 0.002). Colonization samples were not predictive for the invasive species in 11/100 cases. Fifty-six percent of the patients had undergone surgery, 51% were intensive care unit (ICU) patients, and 33% had malignant disease. Mortality increased by age (P = 0.009) and varied by species (36% for C. krusei, 25% for C. parapsilosis, and 14% for other Candida species), severity of underlying disease (47% for ICU patients versus 24% for others; P = 0.0001), and choice but not timing of initial therapy (12% versus 48% for patients with C. glabrata infection receiving caspofungin versus fluconazole; P = 0.023). The initial antifungal agent was deemed suboptimal upon species identification in 15% of the cases, which would have been 6.5% if current guidelines had been followed. A large proportion of Danish fungemia patients were severely ill and received suboptimal initial antifungal treatment. Optimization of diagnosis and therapy is possible