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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining, MLDM 2003"

共 1150 条记录，以下是111-120 订阅

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learning multi-label alternating decision trees from texts and data

引用

3rd international conference on machine learning and data mining in Pattern Recognition

作者： De Comité, F Gilleron, R Tommasi, M Univ Lille 1 F-59655 Villeneuve Dascq France Univ Lille 3 F-59653 Villeneuve Dascq France

ISBN: (纸本)3540405046

Multi-label decision procedures are the target of the supervised learning algorithm we propose in this paper. Multi-label decision procedures map examples to a finite set of labels. Our learning algorithm extends Schapire and Singer's *** and produces sets of rules that can be viewed as trees like Alternating Decision Trees (invented by Freund and Mason). Experiments show that we take advantage of both performance and readability using boosting techniques as well as tree representations of large set of rules. Moreover, a key feature of our algorithm is the ability to handle heterogenous input data: discrete and continuous values and text data.

关键词： boosting alternating decision trees text mining multi-label problems

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Application of data mining Classification Algorithms for Breast Cancer Diagnosis 18

Application of Data Mining Classification Algorithms for Bre...

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3rd international conference on Smart City Applications (SCA')

作者： Saoud, Hajar Ghadi, Abderrahim Ghailani, Mohamed Abdelhakim, Boudhir Anouar UEA LIST Lab Tangier Morocco UEA LabTIC Lab Tangier Morocco

ISBN: (纸本)9781450365628

Breast cancer is one of the diseases that represent a large number of incidence and mortality in the world. data mining classifications techniques will be effective tools for classifying data of cancer to facilitate decision-making. The objective of this paper is to compare the performance of different machine learning algorithms in the diagnosis of breast cancer, to define exactly if this type of cancer is a benign or malignant tumor. Six machine learning algorithms were evaluated in this research Bayes Network (BN), Support Vector machine (SVM), k-nearest neighbors algorithm (Kim), Artificial Neural Network (ANN), Decision Tree (C4.5) and Logistic Regression. The simulation of the algorithms is done using the WEKA tool (The Waikato Environment for Knowledge Analysis) on the Wisconsin breast cancer dataset available in UCI machine learning repository.

关键词： Brest cancer diagnostic machines learning algorithms classification WEKA

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The 3rd international Workshop on machine learning on Graphs (MLoG) 23

The 3rd International Workshop on Machine Learning on Graphs...

引用

16th international conference on Web Search and data mining

作者： Derr, Tyler Ma, Yao Rozemberczki, Benedek Shah, Neil Pan, Shirui Vanderbilt Univ Nashville TN 37235 USA New Jersey Inst Technol Newark NJ USA Isomorph Labs London England Snap Inc Santa Monica CA USA Griffith Univ Nathan Qld Australia

ISBN: (纸本)9781450394079

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd international Workshop on machine learning on Graphs (MLoG)(1), held in conjunction with the 16th ACM conference on Web Search and data mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.

关键词： machine learning on Graphs

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An extensible service oriented distributed data mining framework

An extensible service oriented distributed data mining frame...

引用

3rd international conference on machine learning and Applications

作者： Kumar, A Kantardzic, M Ramaswamy, P Sadeghian, P Univ Louisville Dept Comp Sci & Comp Engn JB Speed Sch Engn Louisville KY USA

ISBN: (纸本)0780388232

This paper discusses a new approach for developing a service-oriented infrastructure for distributed data mining applications. The proposed architecture hides the complexity of implementation details and enables users to perform data mining in a utility-like fashion. The service-oriented architecture provides an autonomic data mining framework where self-describing data mining services can be automatically discovered on the Internet. Moreover, this structure allows for the implementation of data mining algorithms for processing data on more than one site in a distributed manner. The performance of the proposed distributed data mining framework is compared to a standard data mining approach to demonstrate its effectiveness.

关键词： data mining

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Prediction of Students' Employment Prospect Based on GBDT Algorithm Model 3

Prediction of Students' Employment Prospect Based on GBDT Al...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Liu, Chang Wei, Zhengang Ocean Univ China Dept Comp Applicat Technol Qingdao Shandong Peoples R China Ocean Univ China Dept Software Engn Qingdao Shandong Peoples R China

ISBN: (纸本)9781665417907

The important indicator of students' employment prospect can help colleges and universities better stabilize the output of college students who meet the actual needs of society [1]. The definition of students' employment prospect and its influencing factors is not obvious. Based on this, this paper selects indicators, constructs three types of indicators such as career choice, salary and self realization to form students' employment prospect indicators, and uses random forest, SVM and gbdt models for analysis. The comparative analysis of data shows that gbdt model has good prediction ability, The first mock exam shows that this model is more suitable for the need of educational data mining.

关键词： Big data data mining Students' employment prospects Prediction model

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SmartBlend: Smart Video Recommendations on YouTube 3

SmartBlend: Smart Video Recommendations on YouTube

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Singh, Kulvinder Maurya, Aditya Kumar Solanki, Drishti Chandigarh University Dept of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350375190

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. A transformative approach in online education by integrating Multimodal data mining and data Synthesis to develop a Smart System for learning Video Recommendations. With a focus on Personalized learning and Adaptability, the methodology employs advanced techniques like Multimodal learning Analytics and machine learning for a comprehensive analysis of learning materials. SmartBlend introduces a novel approach to personalized learning video recommendations on YouTube, leveraging Multimodal data mining and data Synthesis. The SmartBlendEdu framework employs advanced techniques like Multimodal learning Analytics and machine learning for a comprehensive analysis of learning materials. © 2024 IEEE.

关键词： data mining

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Construction Model Using machine learning Techniques for the Prediction of Rice Produce for Farmers 3

Construction Model Using Machine Learning Techniques for the...

引用

3rd IEEE international conference on Image, Vision and Computing (ICIVC)

作者： Inyaem, Uraiwan Rajamangala Univ Technol Thanyaburi Fac Sci & Technol Dept Math & Comp Sci Pathum Thani Thailand

ISBN: (纸本)9781538649916

Nowadays, the researches in data mining area have been continuous increasing. Appling data mining to agriculture;for example, the prediction of rice produce for farmers is still challenging. The objective of the research is to propose a model using machine learning Techniques comparing between Decision Tree Technique and Neural Network Technique (ANN) for the prediction of rice produce for farmers. Farmers can predict volume of rice produce and selling price. It is helpful for farmers to increase their income. The process of the research follows Cross-industry standard process for data mining (CRISP-DM) process. The model pattern is classified by machine learning techniques experiment with a dataset of farmer records. Performance measure of model pattern uses four options such as Test Options, Cross-Validation Folds 10, Split 80-20, and Use Training Set. After that, four options will be averaged for accuracy. The experimental result shows that the best technique which has highest accuracy can be helpful for farmers in real world.

关键词： data classification machine learning decision tree technique rice product prediction

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Integration of Complex System Simulation and data mining 3

Integration of Complex System Simulation and Data Mining

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Zhang, Fa Zhuhai Sch Beijing Inst Technol Dept Business Adm Zhuhai Peoples R China

ISBN: (纸本)9781665417907

Simulation is a common method for studying the behavior of complex systems and revealing the mechanism of the system. However, complex systems have many parameters, non-linear interactions, and complex evolutionary dynamics. It is difficult to reveal the mechanism of complex systems. Especially complex system simulation experiments may produce a large amount of data. How to summarize the macroscopic mode of the system, identify key factors, and discover the relationship between input and output variables, still lacks an effective method. This paper proposes an integrated framework for simulation modeling and data mining, which combines data mining and simulation modeling to conduct iterative experimental exploration and analysis of complex systems. data mining techniques were used in multiple stages ofmodeling and simulation, including: ETL on raw data, text mining and process mining to build conceptual models, uniform experimental design to generate simulation data, and clustering ofsimulation data to identify system macro patterns, use stepwise regression, neural network, etc. to build a meta-model of a complex system. The introduction of data mining can improve the ability and efficiency of complex system modeling and simulation.

关键词： simulation complex system data mining

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Comparative Study of Classifier for Chronic Kidney Disease prediction using Naive Bayes, KNN and Random Forest 3

Comparative Study of Classifier for Chronic Kidney Disease p...

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3rd international conference on Computing Methodologies and Communication (ICCMC)

作者： Devika, R. Avilala, Sai Vaishnavi Subramaniyaswamy, V SASTRA Deemed Univ Sch Comp Thanjavur Tamil Nadu India

ISBN: (纸本)9781538678084

Chronic kidney disease (CKD), is also known as chronic nephritic sickness. It defines constrains which affects your kidneys and reduces your potential to stay healthy. There will be various complication concerns like increased levels in your blood, anemia (low blood count), weak bones, and nerve injury. Detection and treatment should be done prior so it will typically keep chronic uropathy from obtaining a worse condition. data processing is the term used for information discovery from big databases. The task of knowledge mining is to generate regular patterns from historical data and emphasize future conclusions, follows from the convergence of many recent trends: the decreased value of huge knowledge storage devices and therefore the tremendous ease of aggregation knowledge over networks;the development of robust and economical machine learning algorithms to method this data;and therefore the decrease value of machine power, enabling use of computationally intensive strategies for knowledge analysis. machine learning is an important task as it benefits many applications such as analyzing life science outcomes, sleuthing fraud, sleuthing faux users etc. varied knowledge mining classification approaches and machine learning algorithms are applied for prediction of chronic diseases. Therefore, this paper examines the performance of Naive Bayes, K-Nearest Neighbour (KNN) and Random Forest classifier on the basis of its accuracy, preciseness and execution time for CKD prediction. Finally, the outcome after conducted research is that the performance of Random Forest classifier is finest than Naive Bayes and KNN

关键词： data mining machine learning Chronic kidney disease Classification K-Nearest Neighbour Naive Bayes Random Forest

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Opinion mining from student feedback data using supervised learning algorithms 3

Opinion mining from student feedback data using supervised l...

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3rd MEC international conference on Big data and Smart City (ICBDSC)

作者： Dhanalakshmi, V Bino, Dhivya Saravanan, A. M. Middle East Coll Dept Comp Muscat Oman Caledonian Coll Engn Muscat Oman

ISBN: (纸本)9781467395847

This paper explores opinion mining using supervised learning algorithms to find the polarity of the student feedback based on pre-defined features of teaching and learning. The study conducted involves the application of a combination of machine learning and natural language processing techniques on student feedback data gathered from module evaluation survey results of Middle East College, Oman. In addition to providing a step by step explanation of the process of implementation of opinion mining from student comments using the open source data analytics tool Rapid Miner, this paper also presents a comparative performance study of the algorithms like SVM, Naive Bayes, K Nearest Neighbor and Neural Network classifier. The data set extracted from the survey is subjected to data preprocessing which is then used to train the algorithms for binomial classification. The trained models are also capable of predicting the polarity of the student comments based on extracted features like examination, teaching etc. The results are compared to find the better performance with respect to various evaluation criteria for the different algorithms.

关键词： Sentiment analysis Opinion mining Supervised learning machine learning Natural Language Processing Rapid Miner Text Analytics learning Analytics

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