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Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness.
Specifically, we pose the selection of potent local descriptors as filtering based feature selection problem which ranks the local features per category based on a novel measure of distinctiveness.
This paper presents a novel meta learning framework for feature selection (FS) based on fuzzy similarity.
We study the identification of direct and indirect causes on time series and provide necessary and sufficient conditions in the presence of latent variables.
In this work, the performance of a CNN was investigated for classification and regression analysis of spectral data.
Feature selection is one of the most challenging issues in machine learning, especially while working with high dimensional data.
Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database of screening mammography (DDSM) is used for evaluation.
In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets.
The optimized framework was also compared with other six classical filter-based feature selection methods.
In any multi-script environment, handwritten script classification is of paramount importance before the document images are fed to their respective Optical Character Recognition (OCR) engines.