[PDF] Top 20 Semi Supervised Classification in Educational Data Mining: Students’ Performance Case Study
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Semi Supervised Classification in Educational Data Mining: Students’ Performance Case Study
... and supervised techniques and it aims to utilize from both of ...used semi supervised learning methods have different types called as self-training, co-training, transductive support vector machines, ... See full document
5
Data Mining Technique for Prediction of Academic Performance of Student using SOM
... Educational data mining is the emerging field regarding to prediction of future performance The objective of the proposed methodology is to build the classification model that ... See full document
6
Predicting Dropout Students Using Data Mining Techniques
... of data stored in educational database increasing ...of students’ performance. Educational data mining is used to study the data available in the ... See full document
7
Performance of Ontology classification for Self-Assessment in Educational Data Mining
... evaluation study of Studio will provide additional insights into the usage of the system by the ...new data it is possible to mine profiles over time on the knowledge ... See full document
9
Educational Data Mining Students Performance Prediction
... the students’ performance prediction has been carried out successfully and results state that naïve bayes and decision tree gave the best result for the proposed research ...multiple classification ... See full document
14
Student’s Performance Analysis using Decision Tree Algorithms
... from data originating from educational ...of data mining approach to study students’ performance in CSC207 (Internet Technology and Programming I) a 200 level course in ... See full document
8
Educational Data Mining & Students Performance Prediction using SVM Techniques
... selected data mining algorithms for classification on the university sample data reveal that the prediction rates are not remarkable (vary between 52-67 ...The data attributes related ... See full document
7
An Experimental Analysis of Various Algorithms for Classification in Educational Data Mining with the help of LMS
... individual data are collected. The qualities are MED (Medium of Study), FG (First Graduate), RESI (Residence), LIVLOC (Living Location), FSIZE (Family Size), FEDU (Fathers Qualification), MEDU (Mothers ... See full document
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Data mining techniques to improve predictions accuracy of students' academic performance : a case study with Xorro Q : a thesis presented in partial fulfilment of the requirements for Master of Information Science (IT) at Massey University, Auckland, New Zealand in 2018
... This study is in the educational domain where student-related course data has been used to extract insights on student performances over the study ...sive data collected from an ... See full document
17
Educational Data Mining for Classification of Students based on their Performance
... Educational Data Mining is used for describing the research disciplines which uses data from educational ...the data collected from different educational sources and he ... See full document
7
International Journal of Computer Science and Mobile Computing
... Data Mining could be used to improve business intelligence process including education system to enhance the efficacy and overall efficiency by optimally utilizing the resources ...The performance, ... See full document
5
A Survey on Data Stream and Its Various Techniques
... D ata streams perceive concept-drift, which occurs when the essential concept of the data changes over time. In order to address concept-drift, a classification model must continuously update itself to the ... See full document
6
A Comparative Study of Classification Techniques in Data Mining Algorithms
... in data mining and a study on each of them. Data mining can be used in a wide area that integrates techniques from various fields including machine learning, Network intrusion ... See full document
7
A SURVEY ON THE CLASSIFICATION TECHNIQUES IN EDUCATIONAL DATA MINING
... student’s performance in examination. In data collection, a slight modification has been done in defining the nominal values for the analysis of ...system, data is preprocessed, and integer values ... See full document
7
Semi Stacking for Semi supervised Sentiment Classification
... and data mining communities due to its inherent challenges and wide applica- tions (Pang et ...sentiment classification, which aims to determine the senti- mental orientation a piece of text ... See full document
5
Active Deep Networks for Semi Supervised Sentiment Classification
... a classification method based on deep neural net- work, this result proves the good learning ability of deep ...of semi-supervised learning and active learning based on deep architecture, the ... See full document
9
Methods and Systems for Fault Diagnosis in Nuclear Power Plants
... final classification results by the central station (corresponding to the measurement signal above) are shown in the Figure 4-4 ...this case, using ... See full document
234
An Inexact Implementation of Smoothing Homotopy Method for Semi Supervised Support Vector Machines
... . We take randomly 30% of them as labeled and the remaining 70% as unlabeled. The com- parisons of our method with the LSVM method from [15] without the consideration of unlabeled data are given. Final results ... See full document
7
International Journal of Computer Science and Mobile Computing
... Customer analysis is an unsupervised learning technique (Tsai et al., 2011). Customer analysis refers to identifying groups of customers with similar characteristics (Ahn and Sohn, 2009), splitting the full data ... See full document
10
A Review of Data Mining and its Methods Used in Manufacturing and How Warehousing Impacts Manufacturing
... by data driven models where these models have been of benefitting advantage in decision making for optimization of complex manufacturing ...heterogeneous data-sets using measures of density that examines ... See full document
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