Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods

Root canal morphology of primary maxillary second molars:a micro-computed tomography analysis

Aim Successful endodontic treatment of primary teeth requires comprehensive knowledge and understanding of root canal morphology. The purpose of this study was to investigate the root canal configurations of primary maxillary second molars using micro-computed tomography. Methods Extracted human primary maxillary second molars (n = 57) were scanned using micro-computed tomography and reconstructed to produce three-dimensional models. Each root canal system was analysed qualitatively according to Vertucci's classification. Results 22.8% (n = 13) of the sample presented with the fusion of the disto-buccal and palatal roots; of these, Type V was the most prevalent classification. For teeth with three separate roots (n = 44), the most common root canal type was Type 1 for the palatal canal (100%) and disto-buccal canal (77.3%) and Type V for the mesio-buccal canal (36.4%). Overall, 7% (n = 4) of mesio-buccal canals were 'unclassifiable'. Conclusion The root canal systems of primary maxillary second molars were not only complex but had a range of configurations that may contribute to unfavourable clinical outcomes after endodontic treatment

Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models

This multidisciplinary study focuses on the application and comparison of several topology preserving mapping models upgraded with some classifier ensemble and boosting techniques in order to improve those visualization capabilities. The aim is to test their suitability for classification purposes in the field of food industry and more in particular in the case of dry cured ham. The data is obtained from an electronic device able to emulate a sensory olfative taste of ham samples. Then the data is classified using the previously mentioned techniques in order to detect which batches have an anomalous smelt (acidity, rancidity and different type of taints) in an automated way

Ndpmine: Efficiently mining discriminative numerical features for pattern-based classification

Abstract. Pattern-based classification has demonstrated its power in recent studies, but because the cost of mining discriminative patterns as features in classification is very expensive, several efficient algorithms have been proposed to rectify this problem. These algorithms assume that feature values of the mined patterns are binary, i.e., a pattern either exists or not. In some problems, however, the number of times a pattern appears is more informative than whether a pattern appears or not. To resolve these deficiencies, we propose a mathematical programming method that directly mines discriminative patterns as numerical features for classification. We also propose a novel search space shrinking technique which addresses the inefficiencies in iterative pattern mining algorithms. Finally, we show that our method is an order of magnitude faster, significantly more memory efficient and more accurate than current approaches. Keywords: Pattern-Based Classification, Discriminative Pattern Mining, SVM