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Basic Tenets of Classification Algorithms k-nearest-neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

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Journal of Data Analysis and Information Processing 2020年第4期8卷 341-357页

作者： Ernest Yeboah Boateng Joseph Otoo Daniel A. Abaye Department of Basic Sciences School of Basic and Biomedical Sciences University of Health and Allied Sciences Ho Ghana Department of Statistics and Actuarial Science University of Ghana Accra Ghana

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: k-nearest-neighbor (kNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. kNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of k for the kNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and kNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

关键词： Classification Algorithms Non-Parametric k-nearest-neighbor Neural Networks Random Forest Support Vector Machines

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Click through Rate Effectiveness Prediction on Mobile Ads Using Extreme Gradient Boosting

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Computers, Materials & Continua 2021年第2期66卷 1681-1696页

作者： AlAli Moneera AlQahtani Maram AlJuried Azizah Taghareed AlOnizan Dalia Alboqaytah Nida Aslam Irfan Ullah khan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal UniversityDammam34221Saudi Arabia

Online advertisements have a significant influence over the success or failure of your ***,it is important to somehow measure the impact of your advertisement before uploading it online,and this is can be done by calculating the Click Through Rate(CTR).Unfortunately,this method is not eco-friendly,since you have to gather the clicks from users then compute the *** is where CTR prediction come in *** CTR prediction relies on the users’log regarding click information *** prediction of CTR is a challenging and critical process for e-advertising platforms these *** prediction uses machine learning techniques to determine how much the online advertisement has been clicked by a potential client:The more clicks,the more successful the ad *** this study we develop a machine learning based click through rate prediction *** proposed study defines a model that generates accurate results with low computational power *** used four classification techniques,namely k nearest neighbor(kNN),Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).The study was performed on the Click-Through Rate Prediction Competition *** is a click-through data that is ordered chronologically and was collected over 10 *** results reveal that XGBoost produced ROC-AUC of 0.76 with reduced number of features.

关键词： Advertisement XGBoost random forest k-nearest-neighbor logistic regression click through rate

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Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

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Computers, Materials & Continua 2023年第3期74卷 5209-5227页

作者： Noor Gul Saeed Ahmed Su Min kim Muhammad Sajjad khan Junsu kim Department of Electronics Engineering Korea Polytechnic UniversityGyeonggi-do15073Korea Department of Electronics University of PeshawarPeshawar25120Pakistan Deparment of Electrical Engineering Mirpur University of Science of TechnologyAJ&KMirpur10250Pakistan Department of Electrical Engineering International Islamic UniversityIslamabad44000Pakistan

A cognitive radio network(CRN)intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum *** CRNs,the secondary users(SUs)opportunistically access the primary users(PUs)***,unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of *** spectrum sensing(CSS)is rated as the best choice for making reliable sensing *** paper employs machinelearning tools to sense the PU channels reliably in *** sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel *** fine-k-nearest neighbor algorithm(FkNN),employed in this paper,estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability *** simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules,validating the effectiveness of the proposed ***,the global decision made at the fusion center(FC)based on the modified sensing samples,results low energy consumption,higher throughput,and improved detection with low error probabilities.

关键词： Energy detection machine learning k-nearest-neighbor decision tree linear regression throughput energy consumption

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On the minimum number of neighbors needed for consensus of flocks

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Control Theory and Technology 2017年第4期15卷 327-339页

作者： Chen Chen Ge Chen Lei Guo Noah’s Ark Laboratory 2012 Lab Huawei Technologies Co. Ltd Beijing China LSC & NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

This paper investigates consensus of flocks consisting of n autonomous agents in the plane, where each agent has the same constant moving speed v and updates its heading by the average value of the kn nearest agents from it, with vn and kn being two prescribed parameters depending on n. Such a topological interaction rule is referred to as k,-nearest-neighbors rule, which has been validated for a class of birds by biologists and verified to be robust with respect to disturbances. A theoretical analysis will be presented for this flocking model under a random framework with large population, but without imposing any a priori connectivity assumptions. We will show that the minimum number of k~ needed for consensus is of the order O（log n） in a certain sense. To be precise, there exist two constants C1 〉 C2 〉 0 such that, if k 〉 C1 logn, then the flocking mode] will achieve consensus for any initial headings with high probability, provided that the speed vn is suitably small. On the other hand, if k 〈 Ca ]ogn, then for large n, with probability 1, there exist some initial headings such that consensus cannot be achieved, regardless of the value of Vn.

关键词： k-nearest-neighbor consensus topological interaction random geometric graph

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Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

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Intelligent Automation & Soft Computing 2023年第8期37卷 2499-2517页

作者： Ammar Almomani Iman Akour Ahmed M.Manasrah Omar Almomani Mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa School of Computing Skyline University CollegeUniversity City of SharjahP.O.Box 1797SharjahUnited Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Information Systems Department College of Computing&InformaticsUniversity of SharjahUnited Arab Emirates Comp.Info Sciences(CIS)Division Higher Colleges of TechnologySharjahUnited Arab Emirates Computer Sciences Department Yarmouk UniversityIrbidJordan Computer Network and Information Systems Department The World Islamic Sciences and Education UniversityAmman11947Jordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network ***,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion *** vast network traffic volume also complicates most network monitoring systems and *** intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these *** study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-nearest-neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning *** stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-nearest-neighbors)and a meta-model represented by the Logistic Regression *** using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,*** obtained results demonstrate improvements in other measurement criteria.

关键词： Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor

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模糊规则在线提取及其在非线性系统辨识中的应用

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自动化仪表 2001年第10期22卷 10-11,16页

作者：黄自元李力雄费敏锐上海大学机电工程及自动化学院上海200072

提出一种从输入 -输出数据提取模糊规则的新方法 :基于k nearest neighbor概念对输入 -输出数据对进行预处理 ;利用竞争学习对输入空间自适应聚类 ;提取高斯基 ;在线调整规则库和模糊推理。将该方法应用于非线性系统辨识。

关键词：模糊规则模糊聚类 k-nearest-neighbor 非线性系统辨识

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