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检索条件"主题词=Classification Algorithms"

共 26354 条记录，以下是211-220 订阅

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Reducing the computation time of the Isodata and K-means unsupervised classification algorithms

Reducing the computation time of the Isodata and K-means uns...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： S. Phillips AT and T Research Laboratories Florham Park NJ USA

Describes a sequence of methods to improve the running-time of the Isodata and K-means algorithms. The methods are compared on a range of remotely-sensed data sets, and show consistent and dramatic speedups.

关键词： classification algorithms Remote sensing Testing Algorithm design and analysis Acceleration Image sensors Statistics Pixel Satellites Image analysis

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Predicting Academic Grades of Students in Computer Programming Using classification algorithms

Predicting Academic Grades of Students in Computer Programmi...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： C P Pavan Kumar Hota V. Asanambigai D. Lakshmi Department of CSE Annamalai University Tamilnadu India School of Computing Science and Engineering (SCSE) VIT Bhopal University Madhya Pradesh India

In computer science education, teaching and learning programming is difficult. Understanding and coding programmes are regarded as extremely difficult in computer science education. This is because practical ability is valued more than theoretical knowledge. According to research, students with metacognitive management skills outperform lower-performing students in programming. The more difficult the programming task, the more important it is for the programmer to have metacognitive control skills. The cognitive processes involved in learning computer programming necessitate the development of metacognitive skills in the novice programmer. This study’s main objective is to predict computer programming students’ academic grades using classification algorithms. The predictive analysis used 151 records that were gathered and used. Three methods are considered in order to find the best classifier: Rule Based classification, Decision Tree classification, and Nave Bayesian classification. The confusion matrix, common metrics like precision, recall, ROC curve, kappa statistics, mean absolute error, root mean squared error, relative absolute error, and root relative squared error are all used to assess how well each classifier classified the dataset. Conclusion: With 95% accuracy, the ID3 classifier outperformed the other six predictive models.

关键词： Measurement Decision making Predictive models Prediction algorithms classification algorithms Computer science education Numerical models

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Severity Prediction of COVID-19 Patients Using Machine Learning classification algorithms: A Case Study of Small City in Pakistan with Minimal Health Facility

Severity Prediction of COVID-19 Patients Using Machine Learn...

引用

International Conference on Computer and Communications (ICCC)

作者： Hina Gull Gomathi Krishna May Issa Aldossary Sardar Zafar Iqbal College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

A bstract-Coronavirus disease has been declared as an infectious pandemic affecting the life and health of millions across the globe. It has caused high number of mortalities giving birth to exceptional state of emergency worldwide. It has not affected the people but also has damaged infrastructure of different countries, especially causing an expectational situation in health care systems globally. Due to unavailability of vaccination and faster human to human transmission of virus, healthcare facilities are at high risk of exceeding their limit and capacity, especially in developing countries like Pakistan. Therefore, it is important to manage resources properly in these countries to control high mortality rate and damage it can cause. In this paper we have taken a case study of small city in Pakistan, where healthcare facilities are not enough to deal with pandemic. Most of the COVID-19 patients have to be refer to big cities based on their severity. We have taken data of COVID-19 positive patients from this small city, developed and applied machine learning classification model to predict the severity of patient, in order to deal with the shortage of resources. Among all seven taken and tested algorithms, we have chosen SVM to predict severity of patients. Model has shown 60% of accuracy and have divided patient's severity into mild, moderate and severe levels.

关键词： COVID-19 Support vector machines Pandemics Urban areas Medical services Machine learning classification algorithms

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Investigation of Shopping Habits Using Data Mining classification algorithms

Investigation of Shopping Habits Using Data Mining Classific...

引用

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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Machine Learning for Enhanced Autism Screening: A Comparative Evaluation of classification algorithms

Machine Learning for Enhanced Autism Screening: A Comparativ...

引用

Cyber Resilience (ICCR), International Conference on

作者： May Y. Al-Nashashibi Mohammad Alauthman Wael Hadi Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Information Security University of Petra Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Detecting autism spectrum disorder (ASD) is challenging due to its varying symptoms across individuals. This research assessed four common machine learning classifiers - Logistic Regression, Naïve Bayes, Support Vector Machines, and K-Nearest Neighbors - using four ASD datasets. Two experiments evaluated the algorithms on the original datasets and versions modified by combining three datasets, adding an age feature, and reducing features. Support vector machines showed the most consistently strong performance on all metrics in both experiments. The adult dataset performed best among original datasets, while the modified combined dataset was most successful, with substantial gains across measures. Overall, the results demonstrate machine learning's promise for developing more accurate and accessible ASD screening, complementing standard diagnostic practices. Further research should evaluate these techniques on expanded and diverse ASD data.

关键词： Support vector machines Autism Logistic regression Machine learning algorithms Machine learning Vectors classification algorithms

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Fuzzy classification algorithms for analysis of polymer spectra

Fuzzy classification algorithms for analysis of polymer spec...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： G. Wirth C.F. Ball D.A. Mlynski Institut für Theoretische Elektrotechnik und Meotechnik Universität Karlsruhe Karlsruhe Germany

We propose an efficient strategy for qualitative analysis of measured polymer spectra based on fast wavelet transforms and fuzzy set theory. First, a wavelet transform is applied to the measured data acting as a feature extractor. Then, a fuzzy classification algorithm separates different spectra into various clusters thus giving a qualitative interpretation of the ingredients due to calculated membership values. For that we implemented a special rule based approach with fuzzy if-then rules, a fuzzy c-means algorithm and a fuzzy Kohonen cluster algorithm. The three classification algorithms are compared and results are given.

关键词： classification algorithms Algorithm design and analysis Polymers Discrete wavelet transforms Clustering algorithms Fuzzy systems Vectors Feature extraction Frequency Wavelet transforms

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Investigation of effect of reducing dataset's size on classification algorithms

Investigation of effect of reducing dataset's size on classi...

引用

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Neelam Singhal Mohd. Ashraf School of ICT Gautam Buddha University Greater Noida India

ISBN: (纸本)9781479968329

Data mining is now one of the most active field of research. Extracting those nuggets of information is becoming crucial and one of its important technique is classification. It helps to group the data in some predefined classes. Various techniques for classification exists which classifies the data using different algorithms. Each algorithm has its own area of best and worst performance. This paper concentrates on the four most famous algorithms, i.e., Decision Tree, Naïve Bayes, K Nearest Neighbour and Genetic Programming and the effect on their performance of time and accuracy when the number of instances are incrementally decreased. This paper will also investigate the difference in result when working with binary class or multiclass datasets and suggest the algorithms to follow when using certain kind of dataset.

关键词： classification algorithms Accuracy Data mining Decision trees Timing Genetic programming Breast cancer

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A Comparative Study on Various Binary classification algorithms and their Improved Variant for Optimal Performance

A Comparative Study on Various Binary Classification Algorit...

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IEEE Region 10 Symposium

作者： Vedant Bahel Sofia Pillai Manit Malhotra Department of Information Technology G H Raisoni College of Engineering Nagpur India Centre of Excellence in AIML Galgotias University Greater Noida India Panjab University Department of Computer Sciences and ApplicationPanjabIndia

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

Machine Learning (ML) has become a vast umbrella of various algorithms. Certainly, even for classification models, there are numerous algorithms such as Logistic Regression, Naïve Bayes Classifier, K-Nearest Neighbors, Decision tree and Random Forest Classifiers. The proposed works present a comparative study of various binary classifier and have implemented various boosting algorithms and finally have summarized the related arguments for optimal performance of the presented classification models.

关键词： classification algorithms Breast cancer Random forests Computational modeling Vegetation Tuning Machine learning algorithms

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Performance Evaluation of classification algorithms on Lymph disease prediction

Performance Evaluation of classification algorithms on Lymph...

引用

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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Chronic Kidney Disease for Collaborative Healthcare Data Analytics using Random Forest classification algorithms

Chronic Kidney Disease for Collaborative Healthcare Data Ana...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： V. Shanmugarajeshwari M. Ilayaraja Kalasalingam Academy of Research and Education Kalasalingam University Krishnankoil Tamil Nadu India

Collaborative Healthcare data analytics is a method of methodical data analysis that allows healthcare specialists to discovery opportunities used for development in health system management processing the various information are stored. This proposed approach entails three parts comparable to preprocessing, attribute selection, classification algorithms. The goal of this work is to plan a machine-based diagnostic approach using machine learning technique. This method is developed to mining the risk factors of chronic kidney diseases. There are many improved algorithms consume stayed proposed in every recent years. These algorithms improve the effectiveness of mining risk of Chronic Kidney Disease, but there are also shortcomings. In this work, Random forest, SVM and ANN algorithms were used to identify an early diagnosis of CKD patients with good accuracy.

关键词： Machine learning algorithms Collaboration Medical services classification algorithms Kidney Random forests Diseases

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