Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud  

作　　者：I.Mettildha Mary K.Karuppasamy 

机构地区：[1]Department of Information Technology,Sri Ramakrishna Engineering College,Coimbatore,Tamilnadu,India [2]Department of Computer Science&Engineering,RVS College of Engineering and Technology,Coimbatore,Tamilnadu,India

出　　处：《Computer Systems Science & Engineering》2023年第9期2667-2685,共19页计算机系统科学与工程（英文）

摘　　要：CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

关 键 词：Cloud analytics machine learning ensemble learning distributed learning clustering classification auto selection auto tuning decision feedback cloud DevOps feature selection wrapper feature selection Adaptive Kernel Firefly Algorithm(AKFA) Q learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP31[自动化与计算机技术—控制科学与工程]