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检索条件"主题词=CLASSIFICATION algorithms"

共 26354 条记录，以下是151-160 订阅

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Sensitivity of hyperspectral classification algorithms to training sample size

Sensitivity of hyperspectral classification algorithms to tr...

引用

Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Matthew A. Lee Saurabh Prasad Lori Mann Bruce Terrance R. West Daniel Reynolds Trent Irby Hemanth Kalluri Electrical and Computer Engineering Department Mississippi State University Mississippi Geosystems Research Institute Mississippi State University Mississippi USA Electrical and Computer Engineering Department Mississippi State University Mississippi USA Plant and Soil Science Department Mississippi State University Mississippi USA

algorithms that exploit hyperspectral imagery often encounter problems related to the high dimensionality of the data, particularly when the amount of training data is limited. Recently, two algorithms were proposed to alleviate the small sample size problem - one is based on employing a Multi-Classifier Decision Fusion (MCDF) in the raw reflectance domain, and the other employed the MCDF framework in the Discrete Wavelet Transform domain (DWT-MCDF). This paper investigates the sensitivity of conventional single classifier based classification approaches, as well as MCDF and DWT-MCDF to variations in the amount of data employed for training the classification system. The hyperspectral data in this experiment was obtained using an airborne hyperspectral imager used by SpecTIRtrade. The results of the experimental analysis show that for the given application, the MCDF and DWT-MCDF algorithms are significantly less sensitive than the conventional algorithms to limited training data. PCA consistently results in overall accuracies of about 35%. LDA accuracies are very high, about 75%, when there is an abundance of training data - about 10X (i.e. number of training samples is 10 times the number of spectral bands); remains above 60% for training data abundances of 2X and higher; but dramatically decreases to ~20% for abundances of 1X. MCDF results in accuracies ranging between 65% and 75% for training data abundance of 3X and higher, but the accuracies drop to ~60% for 2X and ~55% for 1X. DWT-MCDF results in high accuracies with the least sensitivity to training data abundance. Its accuracies range between ~60-65% for abundances of 1X to 10X.

关键词： Hyperspectral sensors classification algorithms Training data Discrete wavelet transforms Hyperspectral imaging Reflectivity Wavelet domain Algorithm design and analysis Principal component analysis Linear discriminant analysis

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New classification algorithms for Developing Online Program Recommendation Systems

New Classification Algorithms for Developing Online Program ...

引用

International Conference on Mobile, Hybrid, and On-line Learning (ELML)

作者： Thomas Meller Eric Wang Fuhua Lin Chunsheng Yang Centre of Educational & Information Technology Douglas College New Westminster Canada School of Business Athabasca University Athabasca Canada School of Computing and Information Systems Athabasca University Athabasca Canada Institute of Information Technology National Research Council Canada Ottawa Canada

This paper presents two novel nearest-neighbor-like classification algorithms for program recommendation in a Web-based system, which provides a program planning service to academic advisors and students of post-secondary institutions. To evaluate the accuracy of classification for program recommendations generated by our algorithm, a statistical study was conducted through comparing our algorithm against two well-known classification algorithms, the Naive Bayes algorithm and the J48 algorithm, for making recommendations to students based on their academic history. The study shows that our proposed nearest-neighbor-like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.

关键词： classification algorithms Inference algorithms Educational institutions History Data mining Nearest neighbor searches Information technology Technology planning Bayesian methods Mobile computing

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Mobile Terminal Session SIR Prediction Method Based on Clustering and classification algorithms

Mobile Terminal Session SIR Prediction Method Based on Clust...

引用

IEEE Conference on Wireless Communications and Networking

作者： David Martín-Sacristán Jose F. Monserrat Daniel Calabuig Narcis Cardona ITEAM-Institute of Telecommunications and Multimedia Applications Universidad Politecnica de Valencia Spain Spanish Ministry of Education and Science iTEAM - Institute of Telecommunications and Multimedia Applications Spain

ISBN: (纸本)9781424463961;9781424463985

In this paper, a novel method for mobile terminal session signal to interference ratio (SIR) prediction is presented. The method is based on clustering and classification algorithms configuring a fully-automatic process, highly adaptable to the deployment scenario. It is focused on the prediction of the session SIR probability density function (pdf) of each user whose knowledge will allow for the application of advanced radio resource management (RRM) techniques. The proposed method has been applied in simulated and real scenarios showing its validity in both cases.

关键词： classification algorithms 3G mobile communication Clustering algorithms Training Prototypes Ground penetrating radar

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Applying Text classification algorithms in Web Services Robustness Testing

Applying Text Classification Algorithms in Web Services Robu...

引用

Reliable Distributed Systems

作者： Nuno Laranjeiro Rui Oliveira Marco Vieira CISUC Department of Informatics Engineering University of Coimbra Portugal

Testing web services for robustness is an effective way of disclosing software bugs. However, when executing robustness tests, a very large amount of service responses has to be manually classified to distinguish regular responses from responses that indicate robustness problems. Besides requiring a large amount of time and effort, this complex classification process can easily lead to errors resulting from the human intervention in such a laborious task. Text classification algorithms have been applied successfully in many contexts (e.g., spam identification, text categorization, etc) and are considered a powerful tool for the successful automation of several classification-based tasks. In this paper we present a study on the applicability of five widely used text classification algorithms in the context of web services robustness testing. In practice, we assess the effectiveness of Support Vector Machines, Naïve Bayes, Large Linear classification, K-nearest neighbor (Ibk), and Hyperpipes in classifying web services responses. Results indicate that these algorithms can be effectively used to automate the identification of robustness issues while reducing human intervention. However, in all mechanisms there are cases of misclassified responses, which means that there is space for improvement.

关键词： classification algorithms Robustness Testing Training Simple object access protocol Text categorization

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Comparison of Sound classification algorithms on NIGENS Dataset

Comparison of Sound Classification Algorithms on NIGENS Data...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Jolin Tiomar Stephanie Angela Justin Anthony Sudijanto Gerry Ezekiel Liwe Dani Suandi Dany Eka Saputra Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study examines the performance of several machine learning and deep learning algorithms in classifying sounds from the NIGENS dataset, which consists of 15 unique sound classes. The implementation included five distinct algorithms: Convolutional Neural Networks (CNN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Long Short-Term Memory Networks (LSTM), and Random Forest. The models' performances are evaluated using classification measures such as Accuracy, precision, recall, F1-score, and Receiver Operating Characteristic (ROC) curves. The Random Forest method demonstrates the best overall accuracy of 86% when compared to other algorithms, with precision, recall, and F1-score values of 0.88, 0.86, and 0.86.

关键词： Support vector machines Deep learning Accuracy Machine learning algorithms Nearest neighbor methods Prediction algorithms classification algorithms Convolutional neural networks Random forests Long short term memory

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A comparitive analysis of classification algorithms on different stimuli for EEG based BCI applications

A comparitive analysis of classification algorithms on diffe...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： E Grace Mary Kanaga J Sthuti Chrysolite B Sharon Jerushah K Reethu Dept. of computer science and engineering Karunya Institute of Technology and Sciences Coimbatore India

Brain computer interface (BCI) is an interface that enables interaction between the human brain and peripheral devices for the restoration, assistance or augmentation of cognitive, sensory, motor and auditory events. This paper provides a comparative study on various classification algorithms to design and develop several BCI applications based on Electroencephalography(EEG) signals for the Event Related Potential (ERP) responses for auditory, somatosensory and cognitive events. In order to record the electrical activity of the brain, several auditory, cognitive, somatosensory tasks were given to the subjects. The present study evaluates the performance metrics of three classification algorithms namely k- nearest neighbors (KNN), Multi-Layer Perceptron(MLP) and Random Forest Classifier. This research is distinctive among its counterparts in this field of research in that this study compares the performance of different classifiers across the different Event Related Potential (ERP) responses for different stimuli.

关键词： Electroencephalography classification algorithms Electric potential Vegetation Hidden Markov models

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Evaluation and Implementation of Optimal classification algorithms for Credit Card Fraud Detection

Evaluation and Implementation of Optimal Classification Algo...

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International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Sampath Kini K Pushpakumar. R R. Senthamil Selvan Praveenkumar G D Basi Reddy. A Department of Computer Science and Engineering Nitte Deemed To be University Karkala India Department of Information Technology Vel Tech Rangarajan Dr .Sangunthala R&D Institute of Science and Technology Avadi India Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Tirupati India Department of Computer Technology Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Nowadays, the credit card industry is more concerned about credit card fraud than any other problem. The main idea is to examine optional techniques used for fraud detection and to identify the different types of credit card fraud. Numerous strategies exist for lowering the prevalence of credit card fraud, each tailored to the specific threats faced by banks and the credit card industry. Credit card fraud prevention is the primary goal of implementing these measures. Existing methods have several unresolved issues that cause legitimate credit card customers to be mistakenly labelled as fraudulent. The amount of fraud in the financial sphere may be inferred from this paper's emphasis on integrating the best classification algorithm out of four distinct methods.

关键词： Industries Privacy Costs Prevention and mitigation Logic gates Credit cards Fraud classification algorithms Data mining Detection algorithms

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An empirical comparison of ensemble classification algorithms with support vector machines

An empirical comparison of ensemble classification algorithm...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhong-Hui Hu Yuan-Gui Li Yun-Ze Cai Xiao-Ming Xu Department of Automation Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0780384032

An ensemble classifier often has better performance than any of the single learned classifiers in the ensemble. In this paper, the trained support vector machine (SVM) classifiers are used as basic classifiers. The ensemble methods for creating ensemble classifier, such as bagging and boosting, etc., are evaluated on two data sets. Some conclusions are obtained. Bagging with SVM can stably improve classification accuracy, while the improvement obtained by boosting with SVM is not obvious. These two methods largely increase space complexity and time complexity. Comparatively, the multiple SVM decision model, training individual SVM classifiers using training subsets obtained by partitioning the original training set, has a better trade-off between the classification accuracy and efficiency.

关键词： classification algorithms Support vector machines Support vector machine classification Bagging Boosting Machine learning classification tree analysis Decision trees Artificial neural networks Machine learning algorithms

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Enhancing Malware Detection Using Machine Learning: A Comparative Study of classification algorithms

Enhancing Malware Detection Using Machine Learning: A Compar...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Shalini Singh Suraj Pal Singh Manu Singh Department of Information Technology ABES Engineering College Ghaziabad UP India Department of Computer Science and Engineering Chandigarh University Mohali Punjab India Department of Computer Science ABES Engineering College Ghaziabad UP India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

This research investigates the efficacy of various machine learning algorithms in detecting malware. Utilizing a dataset of 20,000 observations with 33 features,28 classification algorithms are applied and evaluated their performance based on accuracy, balanced accuracy, ROC AUC, F1 score, and execution time. Our results indicate that Extra Trees Classifier, XGB-Classifier, Bagging Classifier, and DecisionTreeClassifier achieved perfect accuracy, with Decision Tree Classifier also demonstrating optimal execution efficiency. This study underscores the potential of decision tree-based models for efficient and accurate malware detection and suggests further exploration with larger datasets and diverse malware types for more generalized findings.

关键词： Accuracy Machine learning algorithms Malware classification algorithms Decision trees Random forests Bagging

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Enhanced Intrusion Detection in Cloud Security by Optimizing classification algorithms

Enhanced Intrusion Detection in Cloud Security by Optimizing...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Mohammad Ziad Mizher Ali Bou Nassif Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

The primary threat to network security is detecting intrusive activities over the network. Because network models and bandwidths are rapidly evolving, the effectiveness of Intrusion Detection Systems (IDSs) requires intuitive Artificial Intelligence (AI). Furthermore, studies in recent years have shown that parsing network traffic data into an IDS is a productive method for detecting security breaches. Detecting malicious assaults on any system is the first step in securing it. To detect whether a network traffic is intrusive or normal, classification algorithms support the IDSs in classifying network traffic types. In this paper, we display the comparative results obtained from conducting the CSE-CICIDS2018 dataset base, which represents in information security research the most reliable and up-to-date dataset. Applying the new AI trends to evaluate different feature selections and machine learning techniques. The classification algorithms such as Multilayer Perceptron (MLP), Naïve Bayes, Radial Basis Neural Network (RBF), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Decision Tree (J48) algorithms come up to show the unwanted features to be removed from nonbehavioral intrusions and identify the most beneficial combination of qualities for a specific statistical report depending on the accuracy, precision, recall, and ROC curve, leading to the best model used with clarification. Results figured that the best classification algorithm is KNN with an accuracy of 100%.

关键词： Support vector machines Accuracy Machine learning algorithms Intrusion detection Telecommunication traffic Machine learning Nearest neighbor methods Feature extraction classification algorithms Decision trees

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