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检索条件"主题词=Classification Algorithms"

共 26354 条记录，以下是181-190 订阅

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Prediction of Students Final Project Values Based on Errors in Scientific Writing Using Data Mining classification algorithms

Prediction of Students Final Project Values Based on Errors ...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Taqwa Hariguna Berlilana Andhika Rafi Hananto Information Systems Departement Universitas Amikom Purwokerto Purwokerto Indonesia

College databases are quickly amassing more and more data. Some data, which has not been used to raise the caliber of student performance, includes secret information regarding student performance. Data mining is used to examine accessible educational data and reveal any hidden information that may be there. This study uses a data mining classification model to develop rules that can forecast the final project value of program students' information management based on course values that aid in the final project's preparation. The study's analysis of student performance in courses that help them prepare for their final projects will also be included. According to the subject that underpins their final project, this prediction is supposed to assist in determining grades. Predictive analysis employing ID3 has an accuracy of 65.54%, CHAID 70.67%, and Nave Bayes 73.13%, According to research that has been done, the competition from the Naive Bayes algorithm has succeeded in being slightly superior to other algorithms.

关键词： Data mining classification algorithms Data models Prediction algorithms Decision trees Predictive models Databases

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Comparative Analysis of classification algorithms for Classifying Psychotypes

Comparative Analysis of Classification algorithms for Classi...

引用

Smart Data Intelligence (ICSMDI), International Conference on

作者： Kalyani Adawadkar Vaibhav Gandhi Gujarat Technological University Ahmedabad India Parul University Vadodara India

Machine learning is a subdomain of Artificial Intelligence that makes a machine learn with the help of data. classification algorithms follow a supervised learning methodology which allows labels to be assigned to the observations so that unobserved data can be labelled based on the training data. This paper intends to study different classification (Supervised learning) algorithms with the help of the MBTI dataset. MBTI Test is a Meyers Briggs Type Indicator test which helps us to identify an individual based on one of the 16 personality types. 4 classification algorithms namely, k-nearest neighbours. Decision Tree, Support Vector Machine and Random Forest algorithm are implemented on the KPMI Dataset. The evaluation metries (accuracy, precision, recall and f1-score) related to each of the classification algorithms are measured. A comparison of the metrics is tabulated to throw light on the best algorithm for the given dataset. As per the MBTI test, an individual belongs to 1 of 16 personality types based on whether an individual is an extrovert(E)-introvert(I), sensing(S)-intuitive(N), thinking(T)-feeling(F) or judging(J)-perceiving(P). The MBTI dataset is visualized to know the psycho-type of the employees. The visualization helps to identify the highly and rarely found psycho type. It is visualized that in employees of the MBTI dataset, most of the psycho-types are satisfied with their jobs. In the future, this algorithm will help in identifying student personality type, related student behaviour analysis, and its predictions related to their career choice, and remedial measures for improvement in personality.

关键词： Support vector machines Measurement Engineering profession Supervised learning Training data Prediction algorithms classification algorithms

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Comparison of data mining classification algorithms for breast cancer prediction

Comparison of data mining classification algorithms for brea...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Chintan Shah Anjali G. Jivani Information Technology Department Shankersinh Vaghela Bapu Institute of Technology Gandhinagar India The Maharaja Sayajirao University of Baroda Vadodara Gujarat IN

Data mining is an area of computer science with a huge prospective, which is the process of discovering or extracting information from large database or datasets. There are many different areas under Data Mining and one of them is classification or the supervised learning. classification also can be implemented through a number of different approaches or algorithms. We have conducted the comparison between three algorithms with help of WEKA (The Waikato Environment for Knowledge Analysis), which is an open source software. It contains different type's data mining algorithms. This paper explains discussion of Decision tree, Bayesian Network and K-Nearest Neighbor algorithms. Here, for comparing the result, we have used as parameters the correctly classified instances, incorrectly classified instances, time taken, kappa statistic, relative absolute error, and root relative squared error.

关键词： classification algorithms Data mining Breast cancer Decision trees Accuracy Vegetation

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Hybrid classification algorithms for Predicting Student Performance

Hybrid Classification Algorithms for Predicting Student Perf...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： A.Dinesh Kumar R.Pandi Selvam V. Palanisamy Alagappa University Karaikudi Ananda College Devakottai

ISBN: (数字)9781728195377

ISBN: (纸本)9781728195384

Data mining is the progress of instinctively find out valuable information from huge depot. In educational data mining predicting the academic performance of the student is a well-known research. The intention of the research work is to examine the classification algorithms as a hybrid classification. We used Radial Basis Function network, Multilayer Perceptron, C4.5 and Random Forest algorithms for classification. Initially classification accuracy was computed individually by the classification algorithms. The Radial Basis Function network, Multilayer Perceptron, C4.5 and Random Forest Algorithm's individual classification gave the accuracy of 72.9167%, 75.4167%, 75% and 73.125% vice versa. To increase more accuracy of classification algorithm the Radial Basis Function network is combined with multilayer perceptron. This hybrid algorithm provides 75.625% of classification accuracy. Then we combined C4.5 algorithm with random forest algorithm which gives 76.4583% classification accuracy. In this study we found hybrid classification algorithm gives more accuracy than individual classification algorithm.

关键词： Radial basis function networks Forestry Multilayer perceptrons Prediction algorithms classification algorithms Data mining Random forests

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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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A comparative study of classification algorithms in e-health environment

A comparative study of classification algorithms in e-health...

引用

International Conference on Digital Information Processing and Communications (ICDIPC)

作者： Mennat Allah Hassan M. Elemam Shehab Essam M. Ramzy Hamed Technology & Maritime Transport Arab Academy for Science Cairo Egypt

ISBN: (纸本)9781467375054

In the past years, Health services has been converted from an offline paper oriented system to an online fully automated system ranging from maintaining detailed patients' information in a historical data base, online patient care, E-clinics, E-hospitals to mobile health applications in some countries. The use of information technology has the potential to help healthcare organizations improve the quality of its service. In the context of many industries especially the medical industry, improvement in the quality, accountability, accessibility, and efficiency of healthcare services can be directly tagged to the use to the daily upgrading Data Mining techniques. Data Mining can be used in enhancing the quality of the medical services offered through analyzing data and discovering hidden patterns and relationships that can enhance and even change the treatment methods adopted. In this paper ten classification algorithms are applied on a patients' dataset obtained from a public hospital's data base that contains patients both medical and personal information needed for diagnoses and treatment decisions. These algorithms are analyzed using a data mining tool and a comparative study is undertaken to find the classifier that performs the best analysis on the dataset obtained using a set of eight performance metrics to compare the results of each classifier.

关键词： Data mining classification algorithms Medical diagnostic imaging Measurement Organizations Hospitals

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Determining single tuition fee of higher education in Indonesia: A comparative analysis of data mining classification algorithms

Determining single tuition fee of higher education in Indone...

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Conference on New Media Studies (CoNMedia)

作者： Muhammad Nur Yasir Utomo Adhistya Erna Permanasari Eddy Tungadi Irfan Syamsuddin Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia Computer and Network Engineering Study Program Politeknik Negeri Ujung Pandang Makassar Indonesia

ISBN: (纸本)9781509062850

Student's Single Tuition Fee or Uang Kuliah Tunggal (UKT) is a subsidy policy in higher education by the Indonesian government. This policy regulates the tuition fees incurred by each student at each semester in every higher education institutions. Since the cost of UKT expenses is influenced by the financial ability of each student, therefore the cost of education among students must be grouped into several classes. Until recently, there has been no standard to make such classification whereas such determination is an important task to solve by every higher institution in Indonesia. This study aims to compare five data mining classification algorithms (Gaussian Naïve Bayes, Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Decision Tree and SVM) to find the best algorithm for the case of determining the UKT classes. The experiment is conducted using 230 training data and 10-fold cross-validation evaluation. Based on the result, Decision Tree managed to obtain average accuracy value of 0.814 or 81.4%. Finally, Decision Tree is used to classify the UKT classes of3258 data of students.

关键词： classification algorithms Data mining Decision trees Support vector machines Economics Niobium Education

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Network traffic verification based on a public dataset for IDS systems and machine learning classification algorithms

Network traffic verification based on a public dataset for I...

引用

Proceedings of the International Convention MIPRO

作者： I. Fosić D. Ž agar K. Grgić HEP-Telekomunikacije Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technologies Osijek J.J. Strossmayer University of Osijek Osijek Croatia

ISBN: (数字)9789532331035

ISBN: (纸本)9781665484343

One method of monitoring network traffic is to detect anomalies where the primary goal is to distinguish normal from abnormal network traffic. In this research we used classification algorithm with best result with public IDS (Intrusion Detection System) dataset on real network dataset. NetFlow data collected from network devices are a good source of input for machine learning classification algorithms that can identify anomalies with high certainty based on known data flows. In this paper, several machine learning classification algorithms (Random Forest, K-Nearest Neighbors, Naive Bayes and Support Vector Machines) were used, for recognizing nine types of network attacks (traffic anomalies) classified in UNSW-NB15 dataset. The collected NetFlow data of real traffic on network devices and simulated network attacks were tested on a proposed machine learning model to determine the success of detecting security incidents. F2 and AUC (Area under the ROC curve) scores were used to measure the performance of classification algorithms, as UNSW-NB15 is a highly unbalanced learning dataset. In both classification of reference dataset and dataset of real traffic the best result achieved Random Forest algorithm with AUC sore of 97% and 95%.

关键词： Radio frequency Support vector machines Machine learning algorithms Process control Telecommunication traffic Data models classification algorithms

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Research on Image classification algorithms Based on Improved Convolutional Neural Networks

Research on Image Classification Algorithms Based on Improve...

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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Pei Wang Tianjin University of Technology Tianjin China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

Traditional CNNs still face challenges when dealing with complex scenes and high-dimensional data, such as low feature extraction efficiency and model overfitting. To resolve these challenges, an enhanced convolutional neural network architecture, known as MCAPT, is proposed in this paper, with the goal of improving the accuracy and efficiency of image classification. Initially, a comprehensive analysis of the classic CNN model was conducted, revealing its deficiencies in the feature extraction and pooling layers. Consequently, a multi-scale convolution module was introduced, which utilizes convolution kernels of different sizes to extract image features in parallel. This module significantly enhances the model's capacity to represent a wide range of image characteristics. In addition, we employed adaptive pooling technology, which can flexibly adjust the size of the output feature maps, effectively reducing model complexity and computational burden. Finally, to prevent overfitting, this paper also introduces a new regularization method that combines different regularization techniques such as Dropout. This innovative measure significantly improves the model’s generalization ability, making its performance on the dataset more robust. Experimental results show that the improved CNN outperforms traditional CNN models on standard image classification benchmark datasets, verifying the effectiveness and superiority of the proposed algorithm. In summary, this paper not only provides an effective new algorithm for the field of image classification but also offers new insights for the further development of convolutional neural networks. Future research will focus on the real-time application and deployment of the algorithm to meet the urgent demands of the industry for high efficiency and accuracy.

关键词： Measurement Adaptation models Accuracy Convolution Computational modeling Feature extraction classification algorithms Convolutional neural networks Image classification Overfitting

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Examining the Efficiency of Various classification algorithms in Spam Detection Datasets

Examining the Efficiency of Various Classification Algorithm...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mohd. Umar Mahima Gupta Rishabh Didwania Rajat Verma Namrata Dhanda Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India Department of Computer Science and Engineering Amity University Lucknow Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study investigates the relationship between the accuracy of Machine Learning (ML) models used to differentiate between Spam and Normal Messages for different dataset sizes. Efficiency evaluation of four classification models (Decision Tree, Passive Aggressive, Random Forest, and Regression) using 1500, 3000, and 5500 samples from three distinct dataset sizes is performed in this paper. The primary objective is to determine how variations in dataset size impact these models’ accuracy. This work not only quantifies performance shifts but also offers insights into how responsive these models are to changes in data amount through careful experimentation and analysis. These findings enhance understanding of ML model behaviour in identifying unwanted messages and provide practical guidance for selecting and configuring models based on available dataset sizes. They also help to improve the design of trustworthy and accurate categorization systems.

关键词： Analytical models Accuracy Computational modeling Data models classification algorithms Regression tree analysis Random forests

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