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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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Underwater threat recognition: Are automatic target classification algorithms going to replace expert human operators in the near future?

Underwater threat recognition: Are automatic target classifi...

引用

OCEANS - Europe

作者： Olga Lopera Tellez Signal and Image Centre Royal Military Academy Brussels Belgium

In this paper, different human/machine strategies are tested in order to evaluate their performance in underwater threat recognition. Sonar images collected using synthetic aperture sonar (SAS) and side scan sonar (SSS) during real mine countermeasures exercises are used. Data are collected over a test area on the Belgian Continental Shelf, where several targets were deployed. Image resolution is divided in three categories: (1) up to 5cm pixel size, (2) between 5cm and 10cm pixel size, (3) larger than 10cm pixel size. Soil complexity is also evaluated and used to build up different strategies. Results demonstrate the utility of considering the human operator as an integral part of the automatic underwater object recognition process, as well as how automated algorithms can extend and complement human performances.

关键词： classification algorithms Synthetic aperture sonar Sea floor Soil Imaging Databases

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A Comparison of Strategies for Missing Values in Data on Machine Learning classification algorithms

A Comparison of Strategies for Missing Values in Data on Mac...

引用

International Multidisciplinary Information Technology and Engineering Conference (IMITEC)

作者： Tebogo Makaba Eustace Dogo University of Johannesburg Johannesburg South Africa

ISBN: (数字)9781728100401

ISBN: (纸本)9781728100418

Dealing with missing values in data is an important feature engineering task in data science to prevent negative impacts on machine learning classification models in terms of accurate prediction. However, it is often unclear what the underlying cause of the missing values in real-life data is or rather the missing data mechanism that is causing the missingness. Thus, it becomes necessary to evaluate several missing data approaches for a given dataset. In this paper, we perform a comparative study of several approaches for handling missing values in data, namely listwise deletion, mean, mode, k-nearest neighbors, expectation-maximization, and multiple imputations by chained equations. The comparison is performed on two real-world datasets, using the following evaluation metrics: Accuracy, root mean squared error, receiver operating characteristics, and the F1 score. Most classifiers performed well across the missing data strategies. However, based on the result obtained, the support vector classifier method overall performed marginally better for the numerical data and naïve Bayes classifier for the categorical data when compared to the other evaluated missing value methods.

关键词： Measurement Mice classification algorithms Support vector machines Data models Radio frequency Machine learning

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Performance Comparison of classification algorithms for Diagnosing Chronic Kidney Disease

Performance Comparison of Classification Algorithms for Diag...

引用

International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Justin D. de Guia Ronnie S. Concepcion Argel A. Bandala Elmer P. Dadios Electronics and Communications Engineering Department De La Salle University Manila Philippines Manufacturing Engineering and Management Department De La Salle University Manila Philippines

ISBN: (数字)9781728130446

ISBN: (纸本)9781728130453

Chronic kidney disease (CKD) is one of the diseases with high mortality rate. It is a disease resulted from kidney function loss over a long period of time. The disease shows no symptoms during initial stage. When left not medicated, a person may suffer from other complications such as high blood pressure, anemia, malnutrition, increased risk of cardiovascular disease, cognitive impairment and impaired physical function. Automated diagnosis by using classification algorithms has been an interest of researchers. In this study, six machine learning algorithms were used for classification and its prediction performance was compared based on training time and F1 score, with and without hypertuning the parameters. Of all the six algorithms, KNN has the best F1 score of 0.992248 and minimal training time of 46.999ms. The performance of decision trees was improved with hypertuning, having a F1 score from 0.96 to 0.99. Overall, machine learning algorithms are significant tool to assess chronic kidney disease.

关键词： classification algorithms Kidney Diseases Prediction algorithms Decision trees Multilayer perceptrons Support vector machines

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Comparison of Data Mining classification algorithms for Student Performance

Comparison of Data Mining Classification Algorithms for Stud...

引用

IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Emny Harna Yossy Yaya Heryadi Lukas Computer Science Department Bina Nusantara University Indonesia

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

Student performance has an important role to measure student quality. Student quality can be measured through predictions of student performance. Prediction can be done using data mining techniques. One technique that can be used is the classification method. The research aims to find out which classification model has the best performance related to student performance data. The data used is taken from UCI Machine Learning, namely student performance. The study used seven methods, namely K-nearest neighbor, classification and regression trees, naïve bayes, adaboost, extratree, bernaoulli naïve bayes, random forest. The technology used to compare the seven methods uses Python programming. Testing the performance of methods using cross validation. The results of this study are the comparison of student performance classification algorithms on student math, namely K-Nearest Neighboring of 86.52%, classification and regression tests of 86.08%, naïve bayes of 84.78%, adaboost of 88.04%, extratree of 81.30%, bernaoulli naïve bayes of 79.34%, random forest E of 87.82%, random forest G of 89.78%. Based on these results we know that the best classification method is the random forest G of 89.78%.

关键词： Data mining classification algorithms Prediction algorithms Decision trees Computer science Random forests Data models

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Comparison of Multi-Label classification algorithms for Code Smell Detection

Comparison of Multi-Label Classification Algorithms for Code...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Elife Ozturk Kiyak Derya Birant Kokten Ulas Birant The Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Computer Engineering Dokuz Eylul University Izmir Turkey

Code smells in a source code shows the weakness of design or implementation. To detect code smells, several detection tools have been developed. However, these tools generally produce different results, since code smells are subjectively interpreted, informally defined and configured by the developers, domain-dependent and based on opinions and experiences. To cope with these issues, in this paper, we have used machine learning techniques, especially multi-label classification methods, to classify whether the given source code is affected with more than one code smells or not. We have conducted experiments on four code smell datasets and transformed them into two multi-label datasets (one for method level and the other one for class level). Two multi-label classification methods (Classifier Chains and Label Combination) and their ensemble models performed on the converted datasets using five different base classifiers. The results show that, as a base classifier, Random Forest algorithm performs better than Decision Tree, Naive Bayes, Support Vector Machine and Neural Network algorithms.

关键词： classification algorithms Machine learning Support vector machines Tools Machine learning algorithms Software algorithms Decision trees

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Analysis of classification algorithms for Plant Leaf Disease Detection

Analysis of Classification Algorithms for Plant Leaf Disease...

引用

International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： J. Nithiswara Reddy Karthik Vinod A.S. Remya Ajai Amrita Vishwa Vidyapeetham Amritapuri India

Agribusiness is the essential occupation in India, that assumes a vital job in the economy of the nation. Yearly 15.7 percentage of the crops are being lost due to attack by insect pests and diseases [1]. The diseases caused will lead to a reduction of quality and quantity of crops. To maintain the health of the plant, it is required to identify the infection and give reasonable consideration. It is difficult to do physically because the human eye cannot observe the minute variations of the infected part of the leaf. In this way, we have built up a framework programming utilizing Matlab [2] to distinguish plant leaf illnesses by utilizing picture handling procedures. The software is produced so that a man even who don't have earlier learning about the plants, and their ailments can effectively recognize infected leaves. We have utilized k-means clustering to distinguish the tainted region of the plant leaf. The diseased recognition part incorporates picture obtaining, image pre-processing, segmentation and feature extraction and SVM classification.

关键词： Diseases Image segmentation Feature extraction Image color analysis Training Support vector machines classification algorithms

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Investigation of Shopping Habits Using Data Mining classification algorithms

Investigation of Shopping Habits Using Data Mining Classific...

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International Informatics and Software Engineering Conference (UBMYK)

作者： Gamzepelin Aksoy Pınar Ataş Murat Karabatak Yazılım Mühendisliği Fırat Üniversitesi Elazığ Türkiye

Data mining is the discovery of interesting and valuable information hidden in large data sets. Data mining, whose usage area is expanding day by day, is also widely used in the shopping sector. In this paper, a data collection form related to shopping habits was prepared and applied to individuals and a data set was obtained. The data obtained from this form were analyzed using data mining techniques. Thus, it was tried to determine what kinds of products people spend their money, tendency to save money according to gender and what they attach importance to shopping. In this study, many classification algorithms were used and as a result, J48, Naive Bayes, SMO and Random Forest classification algorithms were found to be the highest performing algorithms. The results revealed that gender and occupational knowledge affect the shopping rate and that the budget allocated to shopping varies according to gender. In addition, it was observed that the educational status and place of residence did not affect shopping tendency.

关键词： Data mining classification algorithms Google Nanoelectromechanical systems Data collection Kalman filters Java

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Application of Machine Learning with Supervised classification algorithms: In the Context of Health

Application of Machine Learning with Supervised Classificati...

引用

International Engineering, Sciences and Technology Conference (IESTEC)

作者： Denis Cedeno-Moreno Miguel Vargas-Lombardo Gises Ciditic Facultad de Ingeniería de Sistemas Computacionales Universidad Tecnológica de Panamá Panamá Panamá

ISBN: (纸本)9781728116921

Nowadays a great amount of data related to health issues is stored, and each time the volume is increasing. In Panama, diabetes is a disease that causes a considerable number of deaths per year. This disease is the fifth cause of death in the country. Diabetes is one of the diseases with the greatest socio-sanitary impact, both due to the great importance it has, and also due to the large number of chronic complications that the patient has and in addition to its high mortality rate. Diabetes is a silent disease and every day in our country there are more people who suffer from it, it is unfortunate that many young people are developing this disease they do not know they have it. Using innovative technologies such as artificial intelligence (AI) applied to sensitive areas such as health is increasing every day. The new models based on machine learning currently, is growing, however in our countries there are few studies related to the subject. Therefore, this research aims to use various techniques of machine learning and determine how these models can help us to solve health problems.

关键词： Diabetes Machine learning Diseases classification algorithms Software Machine learning algorithms

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Gestational Diabetes Mellitus Prediction Based on Two classification algorithms

Gestational Diabetes Mellitus Prediction Based on Two Classi...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Weiyang Zhong Shanxi Jiang Wei Wu Danhong Peng Tian Xu Jun Wang Gongdao Wang Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing PR China China Tower Corporation Limited Nantong Branch PR China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing PR China Zhongda Hospital Southeast University NanjingSchool of Science China Pharmaceutical University Nanjing PR China

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network.

关键词： classification algorithms Training Neural networks Diabetes Pregnancy Genetic algorithms Sociology

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Imbalanced Dataset Problem in classification algorithms

Imbalanced Dataset Problem in Classification Algorithms

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International Informatics and Software Engineering Conference (UBMYK)

作者： Nur Çürükoğlu Medepia IT Consulting İstanbul Türkiye

Nowadays, companies continuous calculations and research with the available data to minimize the cost of personnel and time. Within the company, they provide an environment in which employees can enter their suggestions for improvement or complaints with the purpose of provide better service. Accordingly, use of "Personal suggestion systems" has been increasing by corporate companies during the recent years. In order to automate suggestion systems and make some analyzes, commonly used and developing machine learning technologies are used today. On the other hand, one of the problems encountered of machine learning as is the data set problem having imbalanced distribution. In the real world, data sets have imbalanced are quite a much. In this study, the results of ROS, RUS, SMOTE and ADASYN methods on the classification algorithms were analyzed and the best method "SMOTE" and "Gradient Boosting Classifier" classification algorithm which gave the best results were preferred.

关键词： Static VAr compensators Boosting Logistics Regression tree analysis Random forests classification algorithms

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