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studies in classification, data analysis, and knowledge organization (GfKL 2007).
To achieve highest accuracy in classification data mining techniques have been successfully performed in some special cases, especially in MRI and light microscopy images.
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To better handle uncertainties existing in real classification data and in the membership functions (MFs) in the traditional type-1 fuzzy logic system (FLS), we apply interval type-2 fuzzy sets to construct a type-2 SVMs fusion FLS.
Similar challenges of data complexity impacting classifier performance were also observed in cancer classification data [ 15].
CI_1 and CI_6 differed from each other in case-mix classification, data range, and cost range, whereas CI_1 and CI_8 differed in case-mix classification and cost range.
Chi-square test was used in comparison of classification data.
DRSA proved to be an effective tool in analysis of classification data which are partially inconsistent [ 24, 25].
Applications include singal estimation in spectral data [34]; classification; data compression and noise reduction [35, 36]; identification of spectral signatures [9]; and feature reduction [33].
This list comprised a total of 2,020 features, of which at least 35% were involved in metabolism (KEGG functional classification, data not shown).
BC cases (77 TN, 201 LumA, 101 LumB and 38 HER2+) were selected based on the classification data available in the TCGA cohort description (IHC and PAM50 classification).
Major clustering methods which are applied in classification of data are partition based, hierarchical (agglomerative and divisive) clustering, neural network based, density based, grid based, model based, etc. [29, 30, 31].
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