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Ten percent ranges were the most commonly estimated range interval for all data classification types.
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Working mostly in R, they used a combination of web-scraping for data collection, machine-learning and text mining for data classification, and MTurk for human validation, and were able to construct a viable dataset for North Carolina.
Here we propose an Adaptive Genetic Fuzzy System (AGFS) for optimizing rules and membership functions for medical data classification process.
Canedo et al. [5] propose an ensemble of filters and classifiers for microarray data classification.
For local data, classification was between patients with pre-synaptic dopaminergic deficit and those without.
We propose to use an SVM classifier for pixel-wised classification, which is strongly suitable for hyperspectral data classification [13].
Compressive sensing theory can sample and compress data simultaneously which provide a new approach for big data classification.
Actually, the principal drawback in the use of complex network measures for the data classification is the network formation.
712 out of 7,129 genes were selected for Leukemia data classification and 1,811 out of 18,117 genes were selected for Lung data classification.
Many methods for molecular data classification or clustering based on gene expression data have appeared in this area [ 2– 14].
The major dissimilarity between LDA and PCA is that, generally, PCA is used for feature classification, whereas LDA is used for data classification.
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