When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has...

classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. this paper proposes a three module system for classification of electrocardiogram (ecg) beats. these modules are: denoising module, feature extraction module and a classification module. in the first module the stationary wavelet transform (swf) is used for noise reduction of ...

The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

Semisupervised deep learning methods (DLMs) can mitigate the dependence on large amounts of labeled samples using a small number samples. However, for semisupervised feature (SDFL), quality extracted features cannot be well ensured without certain amount To address this issue, we develop SDFL method with consistency (SDFL-FC) hyperspectral image (HSI) classification. The SDFL-FC first adopts co...

In recent years, production of text documents has seen an exponential growth, which is the reason why their proper classification seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space. Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature spa...