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European Conference on Antennas and Propagation, EuCAP

作者： Raquel C. Conceição Dallan Byrne J. A. Noble Ian Craddock Faculdade de Ciências Universidade de Lisboa Portugal Institute of Biomedical Engineering University of Oxford Oxford UK Department of Electrical & Electronic Engineering University of Bristol UK

In this paper classification algorithms will be used to investigate the presence of tumours in the breast, from signals collected with a radar microwave imaging prototype from the University of Bristol. A number of features will be extracted from the scattering of breast tumours and will then be used in classification algorithms such as Linear Discriminant Analysis or Quadratic Discriminant Analysis. The results from the classifier will allow creating an image of the considered synthetic breast phantom in which normal breast tissue is classified as a “miss” and tumour tissue is classified as a “hit”.

关键词： Breast Phantoms Microwave imaging Radar imaging classification algorithms Tumors

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The method of elf-files identification based on the metric classification algorithms

The method of elf-files identification based on the metric c...

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Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT), Conference of

作者： Igor Zikratov Igor Pantiukhin Irina Krivtsova Nikita Druzhinin ITMO University Saint Petersburg Russia

ISBN: (纸本)9781509025008

When performing the internal audit of computer equipment an important problem is to identify the elf (executable and linkable format) files stored on the investigated hard drive. To solve this problem, we propose a method of identification of unknown elf-ffles based on the metric classification algorithms. The method consists of three stages. On the first stage the preparation of the training sample by the disassembly of each file and submitting it in the form of an ordered set of the 118 elements is implemented. Each of these elements is the frequency of occurrences of the 118 most commonly used commands in the assembler code. Each program in the sample is represented by several sets, corresponding to different versions or operating systems in which this software is installed. Then, the Minkowski metric of each sample file and identifiable file is calculated. For the method of potential functions the selection of the reference elements of each set is obtained. On the third stage using the metric classification algorithms we evaluate affiliation of the identifiable file for a particular program from the sample. To approbate proposed method the experiment with the use of this method was conducted; results showing the accuracy of identification of elf-files was equal to 89,60% were obtained. The results indicate that this method is applicable in problems of identification of elf-files while conducting the internal audit of computer equipment. The advantages of the method are the accuracy of program identification regardless of the elf-files versions in the Linux operating systems. The ease of implementation of our method and the identification execution speed can be used not only in tasks of internal audit, but also in other tasks of computer forensics.

关键词： Training classification algorithms Standards Measurement Computers Operating systems Mathematical model

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Comparative analysis of classification algorithms on tactile sensors

Comparative analysis of classification algorithms on tactile...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Wesley Becari Luana Ruiz Bruno G. P. Evaristo Francisco Javier Ramirez-Fernandez Departamento de Engenharia de Sistemas Eletrônicos Escola Politécnica da Universidade de São Paulo São Paulo Brazil

ISBN: (纸本)9781509015504

A comparative analysis of classification algorithms of iCub platform humanoid hand tactile sensors is presented. The experimental data were analyzed with different learning supervised classification algorithms: Decision Trees Classifiers, k-Nearest Neighbors Classifiers (kNN), and Support Vector Machines (SVM). The best result was obtained with a Gaussian SVM kernel, which allowed 97.4% accuracy using 20% data for holdout validation. The results indicate the potential of categorization and learning of robotic hands for object grasping and manipulation.

关键词： Thumb Support vector machines classification algorithms Tactile sensors Grasping Decision trees

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A survey of classification algorithms for network traffic

A survey of classification algorithms for network traffic

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International Conference on Science Technology Engineering and Management (ICONSTEM)

作者： R. Deebalakshmi V. L. Jyothi Sathyabama University Chennai Jeppiaar Engineering College Chennai

ISBN: (纸本)9781509017072

Network traffic in the world wide is calculated to rise every year twice the times. To keep pace and profit from this increased amount of flows efficiently. And offer new services. Some efficient techniques needed. Day by day new applications are invented and they have heterogeneous nature in network environment and communication between these new devices also a critical part. improving the network performance, establish proper service policies in router, handling network security risks, management of network operations and provide Qos services to users in internet. To solve these issues classification techniques are used. In this survey different classification algorithms are discussed. K-means algorithm, classification using clustering algorithm, classification based on Fuzzy Kernel K-means Clustering, Support vector machine algorithm, and self-learning classifier Bayesian classification, C5.0 and traffic classification using correlation information and robust network traffic algorithms are presented.

关键词： classification algorithms Clustering algorithms Support vector machines Internet Ports (Computers) Partitioning algorithms Algorithm design and analysis

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The effect of class distribution on classification algorithms in credit risk assessment

The effect of class distribution on classification algorithm...

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International Convention on Information and Communication Technology, Electronics and Microelectronics

作者： K. Andric D. Kalpic Privredna Banka Zagreb Radni?ka 50 10000 Zagreb Hrvatska Sveu?ili?te u Zagrebu Fakultet elektrotehnike i ra?unarstva Unska 3 10000 Zagreb Hrvatska

ISBN: (纸本)9781509025435

This paper makes a practical contribution to classification in credit risk assessment by providing empirical evidence on which class distribution in the training sample should be used to maximize performance of different classification algorithms. Data used for credit risk assessment are often imbalanced due to the number of defaulting clients, who represent the minority class, being much less numerous than the non-defaulting majority class. classification algorithms are usually biased towards the majority class and can show deceivingly high overall prediction accuracy, while at the same time exhibiting poor performance in prediction of the minority class. Although altering class distribution can be an effective method for alleviating the adverse impact of class imbalance, limited research efforts have been devoted to empirical evaluation of the role of class distribution in credit risk assessment, especially when dealing with real life data samples. To address this issue, an empirical study on the effect of different proportion of training examples belonging to each class on classifiers' performance is presented. In addition to logistic regression, neural networks and gradient boosting methods were evaluated using several real life and publicly available datasets from the UCI Machine Learning Repository.

关键词： classification algorithms Risk management Logistics Boosting Neurons Training Biological neural networks Sorting algorithm Risk Management Biological neural networks Logistics Boost Risk Assessment Functional training Neurons Power grids

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Comparative analysis of classification algorithms on three different datasets using WEKA

Comparative analysis of classification algorithms on three d...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Rafet Duriqi Vigan Raca Betim Cico Department of Computer Enginering University of Pristina Pristina Kosovo Department of Computer Sciences South East European University Tetovo Macedonia

This paper analyzes the most useful and popular classification algorithms used by the Machine Learning systems, particularly in the artificial intelligent systems. It is focused on the practical applying of classification algorithms, in all three different dataset used throughout this paper. The results of the paper show which algorithm is more convenient for a particular dataset. The common analysis of these datasets including classification algorithms is implemented on the WEKA Tool.

关键词： classification algorithms Algorithm design and analysis Diabetes Testing Machine learning algorithms Training Computers

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Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 86卷 95-106页

作者： Aktepe, Adnan Ersoz, Suleyman Toklu, Bilal Kirikkale Univ Fac Engn Dept Ind Engn TR-71450 Yahsihan Kirikkale Turkey Gazi Univ Fac Engn & Architecture Dept Ind Engn Ankara Turkey

Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction-loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction-loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： classification algorithms Customer satisfaction Customer loyalty Structural Equation Modeling WEKA LISREL

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Twitter sentiment analysis using various classification algorithms

Twitter sentiment analysis using various classification algo...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ajay Deshwal Sudhir Kumar Sharma Department of Computer Science & Engineering KIIT College of Engineering Gurgaon India

ISBN: (纸本)9781509014903

Twitter is a web application built to find out what is happening at any instance through its micro blogging feature, anywhere in the world. Twitter posts are generally short and generated constantly by public and very well-suited for opinion mining. These messages can be classified as containing either positive or a negative sentiment on the basis of certain aspects with respect to a term based query. The past studies of sentiment classification are not very conclusive about which features and supervised classification algorithms are good for designing accurate and efficient sentiment classification system. We propose to combine many feature extraction techniques like emoticons, exclamation and question mark symbol, word gazetteer, unigrams to design more accurate sentiment classification system. This paper presents empirical comparison of six supervised classification algorithms.

关键词： Feature extraction Twitter Sentiment analysis classification algorithms Data mining Computer languages Machine learning algorithms

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Performance Evaluation of Hybrid Bio-Inspired and Deep Learning algorithms in Gene Selection and Cancer classification

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IEEE ACCESS 2025年 13卷 59977-59990页

作者： Alkamli, Shahad S. Alshamlan, Hala M. King Saud Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11543 Saudi Arabia

Cancer classification based on gene expression data is a critical challenge in modern bioinformatics, requiring efficient and accurate feature selection methods. This study explores the performance of hybrid bio-inspired algorithms and deep learning techniques for gene selection and cancer classification. Hybrid bio-inspired methods, inspired by natural optimization processes, have demonstrated significant advantages in navigating high-dimensional genomic data. Meanwhile, deep learning models excel in pattern recognition and automated feature extraction, offering a complementary approach to traditional gene selection techniques. This paper systematically evaluates both approaches, highlighting their strengths and limitations in terms of classification accuracy, computational efficiency, and feature selection effectiveness. Our findings reveal that hybrid bio-inspired methods, such as Grey Wolf Optimizer and Harris Hawks Optimization, achieve high classification accuracy with minimal selected genes, making them computationally efficient for clinical applications. Conversely, deep learning models, including convolutional neural networks and autoencoders, demonstrate superior feature extraction but often require larger datasets and higher computational resources. By providing a comparative analysis, this study aims to guide researchers and clinicians in selecting the most suitable approach for cancer classification tasks. The results underscore the potential of hybrid methodologies in advancing precision oncology while identifying opportunities for future improvements in deep learning-based feature selection.

关键词： classification algorithms Cancer Optimization Deep learning Feature extraction Accuracy Computational modeling Bioinformatics Information filters Genomics Gene selection cancer classification hybrid bio-inspired algorithms deep learning feature selection classification accuracy

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Comparative Analysis of Machine Learning algorithms With Advanced Feature Extraction for ECG Signal classification

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IEEE ACCESS 2024年 12卷 57727-57740页

作者： Subba, Tanuja Chingtham, Tejbanta Sikkim Manipal Univ SMU Sikkim Manipal Inst Technol Dept Comp Sci & Engn Gangtok 737132 Sikkim India

Electrocardiogram is a heartbeat signal that can be used for the application of Human-computer interaction. Electrocardiography (ECG) is a prominent way to analyze heart rate and to diagnose cardiovascular disease. However, its availability has been restricted, especially in contexts with limited resources, due to the cost associated with conventional ECG signal processing equipment. The importance of ECG signal processing classification for improving early diagnoses in clinical and remote monitoring contexts is highlighted here. The dataset considered for this work is MIT-BIH arrhythmia which has 15 categories and summarized in 5 classes Normal (N), Superventricular ectopic beats (SVEB), Ventricular ectopic beat (VEB), Fusion beats (F), and Unknown beats (Q). The work discusses the importance of automated classification techniques that make it possible to analyze vast amounts of ECG data effectively and objectively. This research presents an investigation into the classification of ECG signals using various Machine Learning (ML) methods. Specifically, the performance of Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), K Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms are examined. Among these classifiers, RF exhibits a remarkable accuracy of 98%. The results demonstrate the superior performance of the proposed approach for heartbeat classification systems.

关键词： Electrocardiography Arrhythmia Feature extraction Heart beat Peptides Convolutional neural networks classification algorithms Machine learning Biomedical signal processing Random forests Human computer interaction Electrocardiogram signal processing feature extraction machine learning random forest

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