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classification algorithms in the personalisation of educational portals

INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2016年第1期26卷 105-116页

作者： Debska, Barbara Kubacka, Agnieszka Rzeszow Univ Technol Dept Comp Chem Powstancow Warszawy 6 PL-35959 Rzeszow Poland State Higher Vocat Sch Krosno Dept Informat Inst Technol Wyspianskiego 20 PL-38400 Krosno Poland

Education by means of the e-learning method is becoming more and more popular nowadays and a rapid development of information technologies makes traditional, static websites used for online education being replaced by interactive, intelligent portals. In spite of the rapid advances in informatics, there is still no software which would meet the needs of all learners. Some personalisation features characterise the e-student portal which is addressed to the students of the Informatics Department at a Stanislaw Pigon Higher Vocational State School in Krosno. This paper will present the structure of the portal and also describe how to use it for the personalised online education system.

关键词： blended learning classification algorithms cluster analysis distance education e-learning portals individual learning paths k-means method personalisation of e-learning portal preliminary classification

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Initial Study for the Investigation of Breast Tumour Response with classification algorithms Using a Microwave Radar Prototype 10

Initial Study for the Investigation of Breast Tumour Respons...

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10th European Conference on Antennas and Propagation (EuCAP)

作者： Conceicao, Raquel C. Byrne, Dallan Noble, J. A. Craddock, Ian Univ Lisbon Fac Ciencias Inst Biofis & Engn Biomed Lisbon Portugal Univ Oxford Inst Biomed Engn Oxford OX3 7DQ England Univ Bristol Dept Elect & Elect Engn Bristol Avon England

ISBN: (纸本)9788890701863

In this paper classification algorithms will be used to investigate the presence of tumours in the breast, from signals collected with a radar microwave imaging prototype from the University of Bristol. A number of features will be extracted from the scattering of breast tumours and will then be used in classification algorithms such as Linear Discriminant Analysis or Quadratic Discriminant Analysis. The results from the classifier will allow creating an image of the considered synthetic breast phantom in which normal breast tissue is classified as a "miss" and tumour tissue is classified as a "hit".

关键词： Breast Radar Microwave Imaging classification algorithms

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An Assessment of Decision Tree based classification and Regression algorithms

An Assessment of Decision Tree based Classification and Regr...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Soham Pathak Indivar Mishra Aleena Swetapadma School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9781538649855

ISBN: (纸本)9781538649862

Decision tree as the name suggests are tree structured predictive model. They are one of the most powerful tools in data mining and machine learning, as it is very easy for a person to derive conclusion from the model's result. A decision tree is one of the popular and easily understood algorithms as its results mimic the human brain, hence are easy to understand derive rules out of these results. These algorithms have even influence in medical sector, helping doctors to make a critical decision for a particular pathology report, it's because of its tree nature even a naive person can understand the meaning and derive conclusion from these trees. In this survey an overview and intuition of some of the popular decision tree models like ID3, C4.5, CHAID and CART are discussed. Methods like pruning are discussed which improve the accuracy of model and its necessity.

关键词： Vegetation Regression tree analysis classification algorithms Prediction algorithms Inference algorithms Mathematical model

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Comparative Analysis of classification algorithms on Tactile Sensors 20

Comparative Analysis of Classification Algorithms on Tactile...

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20th IEEE International Symposium on Consumer Electronics (IEEE ISCE)

作者： Becari, Wesley Ruiz, Luana Evaristo, Bruno G. P. Ramirez-Fernandez, Francisco Javier Univ Sao Paulo Escola Politecn Dept Engn Sistemas Eletron Sao Paulo Brazil

ISBN: (纸本)9781509015504

A comparative analysis of classification algorithms of iCub platform humanoid hand tactile sensors is presented. The experimental data were analyzed with different learning supervised classification algorithms: Decision Trees Classifiers, k-Nearest Neighbors Classifiers (kNN), and Support Vector Machines (SVM). The best result was obtained with a Gaussian SVM kernel, which allowed 97.4% accuracy using 20% data for holdout validation. The results indicate the potential of categorization and learning of robotic hands for object grasping and manipulation.

关键词： Tactile sensors classification algorithms Haptic interfaces Machine learning algorithms

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Research and Analysis of Hyperspectral Remote Sensing Image classification algorithms 28

Research and Analysis of Hyperspectral Remote Sensing Image ...

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第28届中国控制与决策会议

作者： Ren Rong School of Computer Science and Engineering Beifang University of Nationalities

ISBN: (纸本)9781467397155

Hyperspectral remote sensing technology is applied to many fields because of its super-multiband,high resolution and vast *** classification technology is a research hotspot *** information is not utilized fully in traditional remote sensing image classification method;so many improved algorithms are disappeared in order to enhance efficiency,accuracy and *** hyperspectral remote sensing image processing flow is ***,demerits and development tendency of classification method are clarified.

关键词： hyperspectral remote sensing classification algorithms pixel spectral feature

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classification algorithms FOR BIG DATA ANALYSIS, A MAP REDUCE APPROACH

CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYSIS, A MAP REDUC...

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Joint ISPRS Conference on Photogrammetric Image Analysis (PIA) and High Resolution Earth Imaging for Geospatial Information (HRIGI)

作者： Ayma, V. A. Ferreira, R. S. Happ, P. Oliveira, D. Feitosaa, R. Costa, G. Plaza, A. Gamba, P. Pontificia Univ Catolica Rio de Janeiro Dept Elect Engn BR-22453 Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Comp & Syst Rio De Janeiro Brazil Univ Extremadura Dept Technol Computers & Commun E-06071 Badajoz Spain Univ Pavia Dept Elect I-27100 Pavia Italy

ISBN: (纸本)9781629935201

Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA's machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

关键词： Big Data MapReduce Framework Hadoop classification algorithms Cloud Computing

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QUEST: Eliminating Online Supervised Learning for Efficient classification algorithms

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SENSORS 2016年第10期16卷 1629-1629页

作者： Zwartjes, Ardjan Havinga, Paul J. M. Smit, Gerard J. M. Hurink, Johann L. Univ Twente Fac Elect Engn Math & Comp Sci NL-7500 AE Enschede Netherlands

In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

关键词： wireless sensor networks unsupervised learning classification algorithms Naive Bayes semi-supervised learning adaptive

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Examining the performance of classification algorithms for imbalanced data sets in web author identification

Examining the performance of classification algorithms for i...

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Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT), Conference of

作者： Alisa A. Vorobeva ITMO University St. Petersburg Russia

ISBN: (纸本)9781509025008

Individuals, criminals or even terrorist organizations can use web-communication for criminal purposes; to avoid the prosecution they try to hide their identity. To increase level of safety in Web we have to improve the author (or web-user) identification and authentication procedures. In field of web author identification the situation of imbalanced data sets appears rather frequent, when number of one author's texts significantly exceeds the number of other's. This is common situation for the modern web: social networks, blogs, emails etc. Author identification task is some sort of classification task. To develop methods, technics and tools for web author identification we have to examine the performance of classification algorithms for imbalanced data sets. In this work several modern classification algorithms were tested on data sets with various levels of class imbalance and different number of available webpost The best accuracy in all experiments was achieved with Random Forest algorithm.

关键词： classification algorithms Training Support vector machines Blogs Text categorization Logistics Authentication

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A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada's Arctic

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2015年第8期36卷 2252-2276页

作者： He, J. Harris, J. R. Sawada, M. Behnia, P. Univ Ottawa Lab Appl Geomat & GIS Sci Ottawa ON Canada Geol Survey Canada Ottawa ON Canada Geofirma Ltd Ottawa ON Canada

To map Arctic lithology in central Victoria Island, Canada, the relative performance of advanced classifiers (Neural Network (NN), Support Vector Machine (SVM), and Random Forest (RF)) were compared to Maximum Likelihood Classifier (MLC) results using Landsat-7 and Landsat-8 imagery. A ten-repetition cross-validation classification approach was applied. classification performance was evaluated visually and statistically using the global classification accuracy, producer's and user's accuracies for each individual lithological/spectral class, and cross-comparison agreement. The advanced classifiers outperformed MLC, especially when training data were not normally distributed. The Landsat-8 classification results were comparable to Landsat-7 using the advanced classifiers but differences were more pronounced when using MLC. Rescaling the Landsat-8 data from 16 bit to 8 bit substantially increased classification accuracy when MLC was applied but had little impact on results from the advanced classifiers.

关键词： classification algorithms LANDSAT satellites COMPARATIVE studies PETROLOGY PERFORMANCE evaluation

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A comparative study of classification algorithms in e-health environment

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

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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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