SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fuzzy Cluster based Nearest Neighbor Classifier

Abstract

In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier (FC-NNC). The effectiveness of the proposed SCDT and FC-NNC is evaluated for leave one out cross validation metric(LOOCV) along with sensitivity, specificity, precision and F1-score with four different classifiers namely 1) Radial Basis Function (RBF), 2) Multi-layer perception(MLP), 3) Feed Forward(FF) and 4) Support vector machine(SVM) for three different datasets of DLBCL, Leukemia and Prostate tumor. The proposed SCDT &FC-NNC exhibits superior result for being considered more accurate decision mechanism.

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

“…e. The F1-Score (5) serves as the harmonic mean of precision and recall, offering a balanced assessment that considers both metrics [23]. By combining precision and recall into a single value, the F1-Score provides a comprehensive measure of a model's performance, particularly in situations where a balance between precision and recall is crucial.…”