Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble

Abstract

High dimension and small sample size is an inherent problem of gene expression datasets which makes the analysis process more complex. The present study has developed a novel learning scheme that encapsulates a hybrid evolutionary fuzzy-rough feature selection model with an adaptive neural net ensemble. Fuzzy-rough method deals with uncertainty and impreciseness of real valued gene expression dataset and evolutionary search concept optimizes the subset selection process. The efficiency of the hybrid-FRGSNN model is evaluated by the proposed neural net ensemble learning algorithm. Again to prove the learning capability of ensemble algorithm, performance of the component classifiers pairing with FR, GSNN and FRGSNN are compared with proposed hybrid-FRGSNN based ensemble model. In addition to this, efficiency of neural net ensemble is compared with two classical and one advanced ensemble learning algorithms.

Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

“…52 In particular, for the given class, the predictive features are conditionally independent from each other and the second assumption asserts that any hidden features cannot affect the prediction model. The NB algorithm has proved its efficiency in variety of application areas such as disease diagnosis, 53 text processing, 54 and image processing. 55 The core functioning of Naı¨ve Bayes algorithm is exemplified as in an instance, the class value is assigned as ''C'' and X is a random variable representing the observed values of the attributes.…”