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classification algorithms FOR BIG DATA ANALYSIS, A MAP REDUCE APPROACH

CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYSIS, A MAP REDUC...

Joint ISPRS Conference on Photogrammetric Image Analysis (PIA) and High Resolution Earth Imaging for Geospatial Information (HRIGI)

作者： Ayma, V. A. Ferreira, R. S. Happ, P. Oliveira, D. Feitosaa, R. Costa, G. Plaza, A. Gamba, P. Pontificia Univ Catolica Rio de Janeiro Dept Elect Engn BR-22453 Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Comp & Syst Rio De Janeiro Brazil Univ Extremadura Dept Technol Computers & Commun E-06071 Badajoz Spain Univ Pavia Dept Elect I-27100 Pavia Italy

ISBN: (纸本)9781629935201

Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA's machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

关键词： Big Data MapReduce Framework Hadoop classification algorithms Cloud Computing

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An Analytic Hierarchy Model for classification algorithms Selection in Credit Risk Analysis

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-7页

作者： Kou, Gang Wu, Wenshuai Southwestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Peoples R China Southwestern Univ Finance & Econ Collaborat Innovat Ctr Financial Secur Chengdu 611130 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China

This paper proposes an analytic hierarchy model (AHM) to evaluate classification algorithms for credit risk analysis. The proposed AHM consists of three stages: data mining stage, multicriteria decision making stage, and secondary mining stage. For verification, 2 public-domain credit datasets, 10 classification algorithms, and 10 performance criteria are used to test the proposed AHM in the experimental study. The results demonstrate that the proposed AHM is an efficient tool to select classification algorithms in credit risk analysis, especially when different evaluation algorithms generate conflicting results.

关键词： ANALYTIC hierarchy process classification algorithms CREDIT risk MULTIPLE criteria decision making DATA mining

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Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第8期80卷 11943-11957页

作者： Arpaci, Ibrahim Huang, Shigao Al-Emran, Mostafa Al-Kabi, Mohammed N. Peng, Minfei Tokat Gaziosmanpasa Univ Dept Comp Educ & Instruct Technol Tokat Turkey Univ Macau Fac Hlth Sci Inst Translat Med Ctr Canc Taipa Macao Peoples R China British Univ Dubai Fac Engn & IT Dubai U Arab Emirates Al Buraimi Univ Coll Dept Informat Technol Al Buraimi Oman Wenzhou Med Univ Zhejiang Taizhou Hosp Taizhou Peoples R China

While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for specialized labs. As an alternative, most of the prior studies have focused on Chest CT images and Chest X-Ray images using deep learning algorithms. However, these two approaches cannot always be used for patients' screening due to the radiation doses, high costs, and the low number of available devices. Hence, there is a need for a less expensive and faster diagnostic model to identify the positive and negative cases of COVID-19. Therefore, this study develops six predictive models for COVID-19 diagnosis using six different classifiers (i.e., BayesNet, Logistic, IBk, CR, PART, and J48) based on 14 clinical features. This study retrospected 114 cases from the Taizhou hospital of Zhejiang Province in China. The results showed that the CR meta-classifier is the most accurate classifier for predicting the positive and negative COVID-19 cases with an accuracy of 84.21%. The results could help in the early diagnosis of COVID-19, specifically when the RT-PCR kits are not sufficient for testing the infection and assist countries, specifically the developing ones that suffer from the shortage of RT-PCR tests and specialized laboratories.

关键词： Machine learning classification algorithms Diagnosis Prediction Novel coronavirus COVID-19

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A context-sensitive offloading system using machine-learning classification algorithms for mobile cloud environment

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 90卷 503-520页

作者： Junior, Warley Oliveira, Eduardo Santos, Albertinin Dias, Kelvin Univ Fed Pernambuco Informat Ctr Rd Jorn Anibal FernandesCidade Univ BR-50740560 Recife PE Brazil

Computational offloading in Mobile Cloud Computing (MCC) has attracted attention due to benefits in energy saving and improved mobile application performance. Nevertheless, this technique underperforms if the offloading decision ignores contextual information. While recent studies have highlighted the use of contextual information to improve the computational offloading decision, there still remain challenges regarding the dynamic nature of the MCC environment. Most solutions design a single reasoner for the offloading decision and do not know how accurate and precise this technique is, so that when applied in real-world environments it can contribute to inaccurate decisions and consequently the low performance of the overall system. Thus, this paper proposes a Context-Sensitive Offloading System (CSOS) that takes advantage of the main machine-learning reasoning techniques and robust profiling system to provide offloading decisions with high levels of accuracy. We first evaluate the main classification algorithms under our database and the results show that JRIP and J48 classifiers achieves 95% accuracy. Secondly, we develop and evaluate our system under controlled and real scenarios, where context information changes from one experiment to another. Under these conditions, CSOS makes correct decisions as well as ensuring performance gains and energy efficiency. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Mobile cloud Context-sensitive Machine-learning classification algorithms

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Predicting risky driving behavior with classification algorithms: results from a large-scale field-trial and simulator experiment

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EUROPEAN TRANSPORT RESEARCH REVIEW 2024年第1期16卷 65页

作者： Garefalakis, Thodoris Michelaraki, Eva Roussou, Stella Katrakazas, Christos Brijs, Tom Yannis, George Natl Tech Univ Athens Dept Transportat Planning & Engn 5 Heroon Polytech Str Athens 15773 Greece UHasselt Transportat Res Inst IMOB Sch Transportat Sci B-3590 Diepenbeek Belgium

Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, and post-trip interventions have gained significant importance in the past few years. This study aimed to analyze different classification techniques and examine their ability to identify dangerous driving behavior based on a dual-approach study. The analysis was based on the investigation of important risk factors such as average speed, harsh acceleration, harsh braking, headway, overtaking, distraction (i.e., mobile phone use), and fatigue. In order to achieve the objective of this study, data were collected through a driving simulator as well as a naturalistic driving study. To that end, four classification algorithms, namely support vector machines, random forest (RFs), AdaBoost, and multilayer perceptron (MLP) neural networks were implemented and compared. In the simulator experiment, RFs and MLPs emerged as the top-performing models with an accuracy of 84% and 82%, respectively, demonstrating its ability to accurately classify driving behavior in a controlled environment. In the naturalistic driving study, RF and AdaBoost maintained robust performance, with high accuracy (i.e., 75% and 76.76% respectively) and balanced precision and recall. The outcomes of this study could provide essential guidance for practitioners and researchers on choosing models for driving behavior classification tasks.

关键词： Driving behavior Random forests Machine learning models classification algorithms Driving simulator study Naturalistic driving study

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Evaluation of classification algorithms for intrusion detection in MANETs

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KNOWLEDGE-BASED SYSTEMS 2012年 36卷 217-225页

作者： Pastrana, Sergio Mitrokotsa, Aikaterini Orfila, Agustin Peris-Lopez, Pedro Carlos III Univ Madrid Dept Comp Sci Leganes Spain Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Secur & Cryptog Lab LASEC Lausanne Switzerland

Mobile Ad hoc Networks (MANETs) are wireless networks without fixed infrastructure based on the cooperation of independent mobile nodes. The proliferation of these networks and their use in critical scenarios (like battlefield communications or vehicular networks) require new security mechanisms and policies to guarantee the integrity, confidentiality and availability of the data transmitted. Intrusion Detection Systems used in wired networks are inappropriate in this kind of networks since different vulnerabilities may appear due to resource constraints of the participating nodes and the nature of the communication. This article presents a comparison of the effectiveness of six different classifiers to detect malicious activities in MANETs. Results show that Genetic Programming and Support Vector Machines may help considerably in detecting malicious activities in MANETs. (C) 2012 Elsevier B.V. All rights reserved.

关键词： MANET Intrusion detection Genetic Programming classification algorithms Support Vector Machines

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Comparative Study of classification algorithms for Quality Assessment of Resistance Spot Welding Joints From Pre- and Post-Welding Inputs

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IEEE ACCESS 2022年 10卷 6518-6527页

作者： Martin, Oscar Ahedo, Virginia Ignacio Santos, Jose Manuel Galan, Jose Univ Valladolid Dept CMeIM EGI ICGF IM IPF Escuela Ingn Ind Ingn Proc Fabricac Valladolid 47011 Spain Univ Burgos Dept Ingn Org Burgos 09006 Spain

Resistance spot welding (RSW) is a widespread manufacturing process in the automotive industry. There are different approaches for assessing the quality level of RSW joints. Multi-input-single-output methods, which take as inputs either the intrinsic parameters of the welding process or ultrasonic nondestructive testing variables, are commonly used. This work demonstrates that the combined use of both types of inputs can significantly improve the already competitive approach based exclusively on ultrasonic analyses. The use of stacking of tree ensemble models as classifiers dominates the classification results in terms of accuracy, F-measure and area under the receiver operating characteristic curve metrics. Through variable importance analyses, the results show that although the welding process parameters are less relevant than the ultrasonic testing variables, some of the former provide marginal information not fully captured by the latter.

关键词： Welding Acoustics classification algorithms Electrodes Steel Boosting Testing Resistance spot welding quality control welding parameters ultrasonic testing tree ensembles stacking

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ON PATTERN classification algorithms - INTRODUCTION AND SURVEY

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1968年第6期AC13卷 676-+页

作者： HO, YC AGRAWALA, AK Division of Engineering and Applied Physics Harvard University Cambridge MA USA

This paper attempts to lay bare the underlying ideas used in various pattern classification algorithms reported in the literature. It is shown that these algorithms can be classified according to the type of input information required and that the techniques of estimation, decision, and optimization theory can be used effectively to derive known as well as new results.

关键词： Pattern classification classification algorithms Automatic control Pattern recognition Artificial intelligence Uncertainty Physics Design engineering Learning Biological systems

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A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第3期18卷 2654-2674页

作者： Ylenia, Colella Chiara, De Lauri Giovanni, Improta Lucia, Rossano Donatella, Vecchione Tiziana, Spinosa Vincenzo, Giordano Ciro, Verdoliva Stefania, Santini Univ Naples Federico II Fac Engn Dept Elect Engn & Informat Technol Naples Italy Univ Naples Federico II Dept Publ Hlth Univ Hosp Naples Italy Univ Naples Federico II Interdept Ctr Res Hlth Management & Innovat Hlth Naples Italy ASL Napoli 1 Ctr Naples Italy

The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations.

关键词： Fuzzy Inference System diabetes health status expert system classification algorithms

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Comparison of classification algorithms for Detection of Phishing Websites

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INFORMATICA 2020年第1期31卷 143-160页

作者： Vaitkevicius, Paulius Marcinkevicius, Virginijus Vilnius Univ Inst Data Sci & Digital Technol Akad Str 4 LT-08412 Vilnius Lithuania

Phishing activities remain a persistent security threat, with global losses exceeding 2.7 billion USD in 2018, according to the FBI's Internet Crime Complaint Center. In literature, different generations of phishing websites detection methods have been observed. The oldest methods include manual blacklisting of known phishing websites' URLs in the centralized database, but they have not been able to detect newly launched phishing websites. More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem on phishing datasets, designed on features extracted from phishing websites' URLs. These studies have shown some classification algorithms performing better than others on differently designed datasets but have not distinguished the best classification algorithm for the phishing websites detection problem in general. The purpose of this research is to compare classic supervised machine learning algorithms on all publicly available phishing datasets with predefined features and to distinguish the best performing algorithm for solving the problem of phishing websites detection, regardless of a specific dataset design. Eight widely used classification algorithms were configured in Python using the Scikit Learn library and tested for classification accuracy on all publicly available phishing datasets. Later, classification algorithms were ranked by accuracy on different datasets using three different ranking techniques while testing the results for a statistically significant difference using Welch's T-Test. The comparison results are presented in this paper, showing ensembles and neural networks outperforming other classical algorithms.

关键词： phishing detection classification algorithms phishing datasets

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