2011
Authors
Gama, J; Rodrigues, PP;
Publication
Encyclopedia of Data Warehousing and Mining, Second Edition
Abstract
2011
Authors
Sebastiao, R; Silva, MM; Gama, J; Mendonca, T;
Publication
2012 25TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)
Abstract
In the clinical practice the concerns about the administration of hypnotics and analgesics for minimally invasive diagnostics and therapeutic procedures have enormously increased in the past years. The automatic detection of changes in the signals used to evaluate the depth of anesthesia is hence of foremost importance in order to decide how to adapt the doses of hypnotics and analgesics that should be administered to patients. The aim of this work is to online detect drifts in the referred depth of anesthesia signals of patients undergoing general anesthesia. The performance of the proposed method is illustrated using BIS records previously collected from patients subject to abdominal surgery. The results show that the drifts detected by the proposed method are in accordance with the actions of the clinicians in terms of times where a change in the hypnotic or analgesic rates had occurred. This detection was performed under the presence of noise and sensor faults. The presented algorithm was also online validated. The results encourage the inclusion of the proposed algorithm in a decision support system based on depth of anesthesia signals.
2011
Authors
Gama, J; Bradley, E; Hollmén, J;
Publication
Lecture Notes in Computer Science
Abstract
2011
Authors
Marques de Sa, JPM; Sebastiao, R; Gama, J; Fontes, T;
Publication
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS
Abstract
We present new results on the performance of Minimum Error Entropy (MEE) decision trees, which use a novel node split criterion. The results were obtained in a comparive study with popular alternative algorithms, on 42 real world datasets. Carefull validation and statistical methods were used. The evidence gathered from this body of results show that the error performance of MEE trees compares well with alternative algorithms. An important aspect to emphasize is that MEE trees generalize better on average without sacrifing error performance.
2011
Authors
Bosnic, Z; Rodrigues, PP; Kononenko, I; Gama, J;
Publication
Advances in Intelligent and Soft Computing
Abstract
Accurately predicting values for dynamic data streams is a challenging task in decision and expert systems, due to high data flow rates, limited storage and a requirement to quickly adapt a model to new data. We propose an approach for correcting predictions for data streams which is based on a reliability estimate for individual regression predictions. In our work, we implement the proposed technique and test it on a real-world problem: prediction of the electricity load for a selected European geographical region. For predicting the electricity load values we implement two regression models: the neural network and the k nearest neighbors algorithm. The results show that our method performs better than the referential method (i.e. the Kalman filter), significantly improving the original streaming predictions to more accurate values. © 2011 Springer-Verlag Berlin Heidelberg.
2011
Authors
Gama, J; Carvalho, A; Krishnaswamy, S; Rodrigues, PP;
Publication
Proceedings of the ACM Symposium on Applied Computing
Abstract
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