Spatial relations are important ingredients for the interpretation of global meaning of structured objects as well as resolving the uncertainty caused by the ambiguities in the feature extraction stage. In this paper, we present a fuzzy rulebased system that accomplished the task of automated linguistic of spatial relationships between each neighboring pair of on-line handwritten stroke characters. We introduced the fuzzy logic in order to evaluate the possible interpretation and precision of the relation itself. The multiclass SVM classifiers are used for ourexperiment to classify the obtained spatial relations. Experiments usingMAYASTROUN database showed that the proposed method produces more intuitive results with a recognition rate of 94.82%. In fact, the experimental results highlighted that our approach outperforms other approaches that are reported in literature.
Ensemble learning has succeeded in the growth of stability and clustering accuracy, but their runtime prohibits them from scaling up to real-world applications. This study deals the problem of selecting a subset of the most pertinent features for every cluster from a dataset. The proposed method is another extension of the Random Forests approach using self-organizing maps (SOM) variants to unlabeled data that estimates the out-of-bag feature importance from a set of partitions. Every partition is created using a various bootstrap sample and a random subset of the features. Then, we show that the process internal estimates are used to measure variable pertinence in Random Forests are also applicable to feature selection in unsupervised learning. This approach aims to the dimensionality reduction, visualization and cluster characterization at the same time. Hence, we provide empirical results on nineteen benchmark data sets indicating that RFS can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art unsupervised methods, with a very limited subset of features. The approach proves promise to treat with very broad domains.
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