Open AccessGenetic Evolutionary Learning Fitness Function (GELFF) for Feature Diagnosis to Software Fault Prediction
Author(s) -
P. Patchaiammal,
R Thirumalaiselvi
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k1233.09811s19
Subject(s) - fitness function , feature selection , computer science , machine learning , feature (linguistics) , artificial intelligence , software , data mining , ranking (information retrieval) , fault (geology) , function (biology) , genetic algorithm , genetic programming , task (project management) , selection (genetic algorithm) , focus (optics) , engineering , biology , paleontology , philosophy , linguistics , physics , systems engineering , optics , evolutionary biology , programming language
Nowadays, proper feature selection f+orFault prediction is very perplexing task. Improper feature selection may lead to bad result. To avoid this, there is a need to find the aridity of software fault. This is achieved by finding the fitness of the evolutionaryAlgorithmic function. In this paper, we finalize the Genetic evolutionarynature of our Feature set with the help of Fitness Function. Feature Selection is the objective of the prediction model tocreate the underlying process of generalized data. The wide range of data like fault dataset, need the better objective function is obtained by feature selection, ranking, elimination and construction. In this paper, we focus on finding the fitness of the machine learning function which is used in the diagnostics of fault in the software for the better classification.