This study investigates the impact of class balancing and regularization on improving the diagnostic agreement in prostate histological images. The U-Net models applied to the Prostate Cancer Grade Assessment dataset ...
This paper addresses the problem of recognizing gestures which are captured using the Kinect sensor in a educational game devoted to the deaf community. Different strategies are evaluated to deal with the problem of h...
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This paper addresses the problem of recognizing gestures which are captured using the Kinect sensor in a educational game devoted to the deaf community. Different strategies are evaluated to deal with the problem of having few samples for training. We have experimented a Leave One Out Training and Testing (LOOT) strategy and an HMM-based ensemble of classifiers. A dataset containing 181 videos of gestures related to nine signs commonly used in educational games is introduced, which is available for research purposes. The experimental results have shown that the proposed ensemble-based method is a promising strategy to deal with problems where few training samples are available.
Misconceptions play a significant role in the learning process as they reflect an inaccurate understanding of a particular concept. Error diagnosis can help teachers and intelligent learning environments determine the...
Misconceptions play a significant role in the learning process as they reflect an inaccurate understanding of a particular concept. Error diagnosis can help teachers and intelligent learning environments determine the most appropriate type of student assistance. Previously, misconceptions were identified using rule-based expert systems (bug libraries) and clustering algorithms. Bug libraries demand extensive work from developers to identify all potential misconceptions and code rules for each one in advance. Additionally, these solutions cannot detect misconceptions for which rules were not explicitly programmed. Clustering-based solutions overcome these drawbacks by automatically identifying misconceptions based on students' most common errors. To effectively and efficiently identify misconceptions, clustering solutions must have a suitable representation of the problem and its steps, and employ machine learning algorithms capable of discerning patterns from them. This paper proposes a solution that utilizes expression trees to represent algebraic problem-solving steps and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify misconceptions by clustering similar errors in a database containing 1064 steps from 112 students. This database was collected from an intelligent learning system designed to assist in solving first-degree equations. In our final solution, a Natural Language Processing tokenizer was employed to represent each term numerically, which identified 178 homogeneous clusters with minimal noise and few outliers.
This paper presents a dynamic ensemble selection method for music genre classification which employs two pools of diverse classifiers. The pools of classifiers are created by using different features types extracted f...
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This paper presents a dynamic ensemble selection method for music genre classification which employs two pools of diverse classifiers. The pools of classifiers are created by using different features types extracted from three distinct segments of each music piece. From these initial pools of weak classifiers, ensembles of classifiers are dynamically selected for each test pattern using the k-nearest oracles method. The experiments compare the performance of different selection strategies on the Latin Music Database to those related to the use of best single classifier, and to the combination of all classifiers in the pool. It was possible to observe that the most promising selection strategy evaluated allows improving the classification accuracy from 63.71% to 70.31%.
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