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IEEE 6th International Advance Computing Conference (IACC)

作者： Srikanth, Panigrahi Deverapalli, Dharmaiah VNR VJIET Dept Informat Technol Hyderabad Telagana India Shri Vishnu Engn Coll Woman Dept Informat Technol Bhimavaram India

ISBN: (纸本)9781467382861

This paper predicts the Diabetes Disease based on Data Mining Techniques of classification algorithms. classification algorithm and tools may reduce heavy work on Doctors. In this paper Evaluated as classification algorithms for the Classify of some Diabetes Disease Patient Datasets. Data Mining is one of the main algorithm is classification. classification algorithm Examine of the Decision Tree algorithm, Byes algorithm and Rule based algorithm. These algorithms are evaluate Error Rates and identify of the patients based evolution Function of the measure the accurate results.

关键词： Pima Diabetes Disease Data set classification algorithm Decision Tree algorithm Byes algorithms and Rules

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A Kind of classification algorithms of Data Mining and Quantitative Analysis

A Kind of Classification Algorithms of Data Mining and Quant...

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3rd International Conference on Advances in Materials Manufacturing (ICAMMP 2012)

作者： Zhang, Yanfeng Li, Tingting Northwestern Polytech Univ Mech & Elect Sch Xian 710072 Peoples R China

ISBN: (纸本)9783037856482

C4.5, Bayesian network and Sequential Minimal Optimization (SMO) are three typical classification algorithms in data mining. Using cross-validation method with 10 folds get analysis and calculation results of the experiments for the three classification algorithms in the same training set and test set. The main metrics include accuracy, precision, speed, robustness, scalability and comprehensibility, we use margin curve show these. It provides a theoretical and experimental basis for users to select a proper classification algorithm with different training sets in quality and amount.

关键词： Data mining classification algorithm Training set Margin curve

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Machine Learning classification algorithms for Phishing Detection: A Comparative Appraisal and Analysis 2

Machine Learning Classification Algorithms for Phishing Dete...

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2nd International Conference of the IEEE-Nigeria-Computer-Chapter (NigeriaComputConf)

作者： Gana, Noah Ndakotsu Abdulhamid, Shafi'I Muhammad Fed Univ Technol Minna Dept Cyber Secur Sci Minna Nigeria

ISBN: (纸本)9781728107134

Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy has been a setback in phishing detection. In this research RandomForest, SysFor, SPAARC, RepTree, RandomTree, LMT, ForestPA, JRip, PART, NNge, OneR, AdaBoostMl, RotationForest, LogitBoost, RseslibKnn, LibSVM, and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms were rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quit a number of classifiers also exist which if properly explored will yield more accurate results for phishing detection. RondomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false positive rate for phishing classification which suggest that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.

关键词： Phishing Attack classification algorithm Accuracy Performance metric RandomForest

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Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of classification algorithms Using Sentinel-2 Data 3rd

Enhancing Forest Fire Detection and Monitoring Through Satel...

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3rd International Conference on Optimization, Learning algorithms and Applications (OL2A)

作者： Brito, Thadeu Pereira, Ana I. Costa, Paulo Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Reg Montanha SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal INESC TEC INESC Technol & Sci Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783031530357;9783031530364

Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules' best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images.

关键词： Machine Learning classification algorithm Satellite Imagery Wildfires Tree Cover Density

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An Investigation of classification algorithms for Intrusion Detection System - A Quantitative Approach

An Investigation of Classification Algorithms for Intrusion ...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Varghese, Josy Elsa Muniyal, Balachandra Manipal Univ Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India

ISBN: (纸本)9781509063673

Nowadays security issues are growing in a tremendous rate. So it is expedient to have a mechanism to keep track of its security issues in the network or host. The Intrusion Detection System (IDS) has a critical part for supervising the networks. The false alarm rate and accuracy are the two important factors to be considered in the design of competent IDS. The role of classification algorithms is indispensable in the decision making of IDS. The redundant and irrelevant features of dataset also affects the performance of classifiers which in turn degrading the evaluation of anomaly detection. The proposed work focuses on the detailed study of different classifiers on two feature selection techniques using NSL-KDD dataset, where Random Forest on Principal Component Analysis (PCA) gives the accuracy rate of 99.52% and false alarm rate is 0.48%.

关键词： Intrusion Detection System(IDS) classification algorithm Feature Selection Technique Principal Component Analysis(PCA) NSL-KDD dataset

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On the Rationality of Explanations in classification algorithms 23

On the Rationality of Explanations in Classification Algorit...

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23rd International Conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Falomir, Zoe Costa, Vicent Univ Jaume 1 ES Tecnol & Cincies Expt Castellon De La Plana Spain Bremen Spatial Cognit Ctr BSCC Bremen Germany Univ Santiago de Compostela Ctr Singular Invest Tecnol Intelixentes CiTIUS Santiago De Compostela Spain CSIC IIIA Artificial Intelligence Res Inst Barcelona Spain

ISBN: (纸本)9781643682112;9781643682105

This paper is a first step towards studying the rationality of explanations produced by up-to-date AI systems. Based on the thesis that designing rational explanations for accomplishing trustworthy AI is fundamental for ethics in AI, we study the rationality criteria that explanations in classification algorithms have to meet. In this way, we identify, define, and exemplify characteristic criteria of rational explanations in classification algorithms.

关键词： Rationality rational explanation explainable AI classification algorithm AI ethics trustworthy AI

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Machine Learning classification algorithms for Adware in Android Devices: A Comparative Evaluation and Analysis 15

Machine Learning Classification Algorithms for Adware in And...

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15th International Conference on Electronics, Computer and Computation (ICECCO)

作者： Ndagi, Joseph Yisa Alhassan, John K. Fed Univ Technol Dept Cyber Secur Sci Minna Nigeria

ISBN: (数字)9781728151601

ISBN: (纸本)9781728151601

Exponential growth experienced in Internet usage has paved the way to exploit users of the Internet, a phishing attack is one of the means that can he used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy have been a setback in phishing detection. In this research 17 different supervised learning techniques such as RandomForest, Systematically Developed Forest (SysFor), Spectral Areas and Ratios Classifier (SPAARC), Reduces Error Pruning Tree (RepTree), RandomTree, Logic Model Tree (LMT), Forest by Penalizing Attributes (ForestPA), JRip, PART, Nearest Neighbor with Generalization (NNge), One Rule (OneR), AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, Library for Support Vector Machine (LibSVM), and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms was rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quite several classifiers also exist which if properly explored will yield more accurate results for phishing detection. RandomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false-positive rate for phishing classification which suggests that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.

关键词： Adware Attack classification algorithm Accuracy Performance metric RandomForest

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A Comparison of classification algorithms Based on The Number of Features 39

A Comparison of Classification Algorithms Based on The Numbe...

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39th Chinese Control Conference (CCC)

作者： Xiong, Peichen Ping, Wei Xian Jiaotong Liverpool Univ Comp Sci & Software Engn Suzhou 215123 Peoples R China Ningbo Univ Coll Sci & Technol Sch Informat Engn Ningbo 315300 Peoples R China

ISBN: (纸本)9789881563903

As the most widely known data milling algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor (KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.

关键词： KNN Decision Tree ID3 classification algorithm

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Construction and Evaluation of a Metabolic Syndrome Prediction Model Based on classification algorithms

Construction and Evaluation of a Metabolic Syndrome Predicti...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Zhao, Zhenghao Hu, Qiongqiong Department of Computer Technology and Application Qinghai University Xining China

Exploring the risk factors influencing Metabolic Syndrome (MS), constructing a risk prediction model based on multiple classification algorithms, comparing the predictive performance of different models for MS, and interpreting the models to derive specific MS prediction rules, providing scientific basis for MS prediction. A retrospective analysis was conducted on clinical data from 2,193 MS patients. Based on whether the patients developed MS, they were divided into a healthy group and an MS group. Statistical correlation tests were used to identify the risk factors associated with MS. Six classification algorithms, including decision trees, logistic regression, random forests, naive Bayes, K-nearest neighbors, and support vector machines, were employed to build an MS prediction model. The prediction model's performance was evaluated using R language by generating receiver operating characteristic (ROC) curves. Among the 2,193 MS patients, the incidence rate of MS was 34.66%. Significant differences (P¡0.05) were observed between the healthy group and the MS group in terms of age, marital status, income, ethnicity, waist circumference, body mass index (BMI), uric acid levels, blood glucose levels, high-density lipoprotein levels, and triglyceride levels. Blood glucose, waist circumference, BMI, and triglycerides showed a significant linear correlation with MS. The ROC curve results indicated that the random forest algorithm achieved an area under the curve (AUC) of 0.94 (95% CI: 0.914-0.957), logistic regression achieved an AUC of 0.90 (95% CI: 0.867-0.925), support vector machines achieved an AUC of 0.89 (95% CI: 0.859-0.920), decision trees achieved an AUC of 0.87 (95% CI: 0.831-0.905), K-nearest neighbors achieved an AUC of 0.81 (95% CI: 0.770-0.850), and naive Bayes achieved an AUC of 0.74 (95% CI: 0.694-0.785). The study results confirmed that factors such as age, marital status, waist circumference, BMI, blood glucose levels, and triglyceride levels are all r

关键词： classification algorithm Metabolic Syndrome Nomogram prediction model statistical analysis

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A colorimetric sensor array combined with a hybrid feature selection approach for discrimination of citrus infested by Bactrocera dorsalis (Hendel)

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 138卷

作者： He, Mi Li, Dapeng Zhang, Xin Jiang, Han Yang, Gan Li, Ling Wen, Tao Cent South Univ Forestry & Technol Coll Mech & Intelligent Mfg Changsha 410083 Hunan Peoples R China

Changes in citrus volatile organic compounds (VOCs) induced by Bactrocera dorsalis (Hendel) infestation can serve as characteristic identifiers for non-destructive detection of infested citrus. This study proposed an innovative method combining colorimetric sensor array (CSA) technology with machine learning algorithms for the discrimination of B. dorsalis infestation in citrus. Gas chromatography-mass spectrometry (GC-MS) analysis identified key VOCs, including d-limonene, linalool, and decanal, as infestation markers. Subsequently, various porphyrin and metalloporphyrin dyes exhibiting sensitivity to these VOCs were selected to construct the CSA. To enhance detection accuracy, a hybrid feature selection method integrating ReliefF and Particle Swarm Optimization (PSO) was implemented. Subsequently, the optimized features subsets were utilized to develop classification models. Specifically, a binary classification model employing the K Nearest Neighbor (KNN) algorithm achieved a high accuracy of 93.89 % in distinguishing between healthy and infected citrus. Furthermore, a multiclass classification model using KNN was developed to differentiate among invasive, incubation, and infestation stages, attaining a remarkable accuracy of 97.78 %. This approach presents a promising solution for early detection of B. dorsalis infestation in citrus.

关键词： Bactrocera dorsalis Colorimetric sensor array Volatile organic compounds ReliefF classification algorithm

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