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检索条件"主题词=Classification Algorithms"

共 26406 条记录，以下是161-170 订阅

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Comparing and analysing binary classification algorithms when used to detect the Zeus malware

Comparing and analysing binary classification algorithms whe...

引用

HCT Information Technology Trends (ITT)

作者： Mohamed Ali Kazi Steve Woodhead Diane Gan Faculty of Engineering and Science University of Greenwich London UK Faculty of Liberal Arts and Sciences University of Greenwich London UK

ISBN: (数字)9781728150611

ISBN: (纸本)9781728150628

The Zeus banking malware is one of the most prolific banking malware variants ever to be discovered. This paper examines and analyses the Support Vector Machine (SVM), Decision Tree and Random Forest machine learning algorithms when used in conjunction with a manual feature selection process to detect Zeus network traffic. Selecting the features manually provides the researcher with more control over which features that can and should be selected. The manual feature selection process will also allow the researcher to analyze the impact of the various features and then select the features that provide the best accuracy results during the classification and detection of Zeus. The algorithms in scope for this research are the Decision Tree algorithm, Random Forest algorithm and the SVM algorithm.

关键词： Malware classification algorithms Feature extraction Banking Telecommunication traffic Support vector machines Servers

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Emotional Text Analysis Based on Ensemble Learning of Three Different classification algorithms

Emotional Text Analysis Based on Ensemble Learning of Three ...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems

作者： WenShuo Bian ChunZhi Wang ZhiWei Ye Lingyu Yan School of computer science Hubei university of technology Wuhan China

In order to improve the accuracy and generalization performance of text sentiment analysis model, an integrated learning model is proposed in this paper, which includes three different classification algorithms - Logistic regression, support vector machine and K-Neighborhood algorithm. Compared with single classification algorithm, this algorithm shows better accuracy. The experimental results show that the model has good generalization performance and robustness.

关键词： classification algorithms Data models Training Support vector machines Prediction algorithms Logistics Machine learning

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A Survey of Hierarchical classification algorithms with Big-Bang Approach

A Survey of Hierarchical Classification Algorithms with Big-...

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International Conference on Science and Technology-Computer (ICST)

作者： Sofi Defiyanti Edi Winarko Sigit Priyanta Faculty of Computer Science Universitas Singaperbangsa Karawang Karawang Indonesia Department of Computer Sciences and Electronics Universitas Gadjah mada Yogyakarta Indonesia

ISBN: (数字)9781728123691

ISBN: (纸本)9781728123707

Hierarchical classification is a technique used to solve problems with hierarchical concepts, which are usually arranged into trees or directed acyclic graphs (DAGs). Research in this field is mostly done in the field of bioinformatics and text classification, because both fields have hierarchical problems. Completion of hierarchy classification can be done with a local approach and a big-bang approach. From several previous studies, it was found that hierarchical classification with the big bang approach got good results, both in terms of predictive accuracy, model size and time needed to build the model. In this paper, we survey previous research on hierarchical classification algorithms using bing-bang approach.

关键词： classification algorithms Prediction algorithms Taxonomy Bioinformatics Text categorization Multiprotocol label switching Predictive models

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Comprehensive Study of Diabetes Miletus Prediction Using Different classification algorithms

Comprehensive Study of Diabetes Miletus Prediction Using Dif...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Sara A. Aboalnaser Hanan R. Almohammadi Department of Computer Science and Engineering College of Computer Science Taibah University Medina Saudi Arabia

ISBN: (数字)9781728130217

ISBN: (纸本)9781728130224

Diabetes mellitus (DM) is a chronic disease. It has been rising more rapidly in middle- and low-income countries. World Health Organization (WHO) [1] estimates that diabetes was the seventh leading cause of death in 2016. In this paper a database concerning this disease where discussed and implemented by data mining techniques. Data mining techniques used to help the prediction of DM. It makes the prediction process faster, cheaper and more accurate for the benefit of both physicians and patients. In this paper, wellknown data mining algorithms explored to achieve DM prediction. The performance of these algorithms was evaluated and discussed using Orange Data Mining tool. The performance evaluation executed using two metrics: the recall and the precision; applied to each discussed classification algorithm. The studied classification algorithms are Naïve Bayes, K-Nearest Neighbours, Artificial Neural Network, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression.

关键词： Diabetes classification algorithms Data mining Artificial neural networks Decision trees Support vector machines Predictive models

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The Effect of Feature Selection on classification algorithms in Credit Approval

The Effect of Feature Selection on Classification Algorithms...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Yoga Pristyanto Sumarni Adi Andi Sunyoto Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia

Data mining applications usually use datasets with large dimensions. Unfortunately, the large dimension in a dataset affects the processing time and outcome of the classification. One solution to this problem is to select relevant features for the reduce dimensions. The selection of the right features can increase the accuracy of the classification process. This study proposes a proper feature selection model for increasing the accuracy for specific classifier models by comparing several existing feature selection models and some of the classifier. The feature selection, we use information gain, gain ratio, and correlation-based feature selection (CBFS) while the classifier we employee K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Artificial Neural Network and Decision Tree. Evaluation is done using several public databases from UCI Machine Learning Repository. Evaluation results indicate that all feature selection methods are increasing the accuracy, but Decision Tree only increases in CBFS. Based on the result, feature selection does not always improve classifier accuracy but depends on the characteristics and algorithms used.

关键词： Feature extraction classification algorithms Support vector machines Data mining Artificial neural networks Correlation Decision trees

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Dangerous Situations Determination by Smartphone in Vehicle Cabin: classification and algorithms 24

Dangerous Situations Determination by Smartphone in Vehicle ...

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24th Conference of Open-Innovations-Association (FRUCT)

作者： Kashevnik, Alexey Karelskaya, Kseniya Repp, Maksim ITMO Univ St Petersburg Russia SPIIRAS St Petersburg Russia

ISBN: (纸本)9789526865386

Nowadays smartphones are widely used in people daily life as they have a lot of sensors that can recognize physical conditions and people in their tasks. One of the task that a smartphone can be utilized is the driver face analysis in vehicle cabin. Together with the information from smartphone sensors such analysis allows to provide recommendations for a driver to prevent the emergency situations. The paper contains the classification of dangerous situations that can be determined by a smartphone as soon as algorithms that can be used for this determination.

关键词： Vehicles classification algorithms Cameras Stress Roads Sensor systems

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Review of Brain Tumor Segmentation, Detection and classification algorithms in fMRI Images

Review of Brain Tumor Segmentation, Detection and Classifica...

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

作者： Tom Philip Pries Roshan Jahan Preetam Suman Universität Osnabrück Osnabrück Germany Integral University Lucknow India

ISBN: (纸本)9781538642559;9781538642542

Nowadays Brain tumor detection in early stage is necessary because many people died due to unawareness of having a brain tumor. On the other side influence of machine learning becomes larger and larger in our lifes and our society, artificial intelligence might also start playing an important role in medical diagnosis and support of doctors and surgeons. This paper is focused on review of those papers which include segmentation, detection and classification of brain tumors. The common procedure for an algorithm which aims to classify brain tumors on fMRI or MRI scans is: Preprocessing the image for example by removing noise, then segmenting the image, which yields the region which might be a brain tumor, and finally classifying features such as inten-sity, shape and texture of this region. Many machine learning approaches towards brain tumor detection have already been made. However, these approaches, even though yielding good results, are not used yet. Therefore this research topic remains important and still requires attention.

关键词： Tumors Feature extraction Image segmentation classification algorithms Functional magnetic resonance imaging Support vector machines Clustering algorithms

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Comparative Study of classification algorithms for Big Data in VANET

Comparative Study of Classification Algorithms for Big Data ...

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International Conference on Advances in Computing and Communication Engineering (ICACCE)

作者： Bahije Najat EL Hadaj Salah Laboratory of Applied Mathematics and Computer Science Cadi Ayyad University Marrakech Morocco

classification is a method used to predict membership of specific instance into a group of data. It uses a supervised learning method. Various of classification algorithm are available to process data, but the issue is which one is preferred taking consideration of the different input parameters. In this paper, we choose the several major supervised algorithms: K-NN (K Nearest Neighbors), NB (Naive Bays) and DT (Decision Tree). We use Vehicle Ad Hoc Network (VANET) real time data. This paper focuses on study of effectiveness measures in terms of accuracy and runtime.

关键词： classification algorithms Decision trees Big Data Vehicular ad hoc networks Runtime Safety

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Comparative Study on Data Mining classification algorithms for Predicting Road Traffic Accident Severity

Comparative Study on Data Mining Classification Algorithms f...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Tadesse Kebede Bahiru Dheeraj Kumar Singh Engdaw Ayalew Tessfaw Dept. of Information Technology Parul University Vadodara Gujarat India

According to World Health Organization report the number of deaths by road traffic accident is more than 1.25 million people and every year with non-fatal accidents affecting more than 20-50 million people. Several factors are contributed on the occurrence of road traffic accident. In this study, data mining classification techniques applied to establish models (classifiers) to identify accident factors and to predict traffic accident severity using previously recorded traffic data. Using WEKA (Waikato Environment for Knowledge Analysis) data mining decision tree (J48, ID3 and CART) and Naïve Bayes classifiers are built to model the severity of injury. The classification performance of all these algorithms is compared based on their results. The experimental result shows that the accuracy of J48 classifier is higher than others.

关键词： Accidents classification algorithms Roads Decision trees Prediction algorithms Data mining Training

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Performance Evaluation of classification algorithms on Lymph disease prediction

Performance Evaluation of classification algorithms on Lymph...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： J. Junia Deborah Latha Parthiban Research Scholar Department of Computer Science Bharathiyar University Head Incharge Department of Computer Science Pondicherry University Community College

ISBN: (纸本)9781538658741

In the last decade, a large number of computer aided diagnosis (CAD) tools are developed for the identification of different algorithm. For the disease diagnosis, classification algorithms based on ML (ML) techniques are commonly used. The classification algorithm uses a supervised learning methodology to makes the system or computer program to learn from the given input data and then employ the learning knowledge to identify the upcoming observations. This paper intends to evaluate the different classification algorithms namely radial basis function (RBF), Naive Bayes (NB), J48 and Olex-GA on the identification of Lymph diseases. For the performance evaluation of different classifiers, a benchmark Lymph dataset is used interms of different performance measures. The obtained results proved that the RBF network attained better performance compared to NB, J48 and Olex-GA.

关键词： classification algorithms Diseases Neurons Lymph nodes Prototypes Training Task analysis

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