A semi-supervised self-trained two-level algorithm for forecasting students' graduation time

[1]  Its'hak Dinstein,et al.  A comparative study of neural network based feature extraction paradigms , 1999, Pattern Recognit. Lett..

[2]  Ludmila I. Kuncheva,et al.  'Change-glasses' approach in pattern recognition , 1993, Pattern Recognit. Lett..

[3]  Claire Cardie,et al.  Weakly Supervised Natural Language Learning Without Redundant Views , 2003, NAACL.

[4]  Xiaojin Zhu,et al.  Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[5]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[6]  Tossapon Boongoen,et al.  Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings , 2017, Int. J. Mach. Learn. Cybern..

[7]  Gürsel Serpen,et al.  Large Experiment and Evaluation Tool for WEKA Classifiers , 2009, DMIN.

[8]  Tassos A. Mikropoulos,et al.  An Ensemble-Based Semi-Supervised Approach for Predicting Students’ Performance , 2018 .

[9]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[10]  Robert Stillwell,et al.  Public High School Four-Year On-Time Graduation Rates and Event Dropout Rates: School Years 2010-11 and 2011-12. First Look. NCES 2014-391. , 2014 .

[11]  Edmund Y. Lam,et al.  Enhancing educational data mining techniques on online educational resources with a semi-supervised learning approach , 2015, 2015 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE).

[12]  Alejandro Peña-Ayala.  Review: Educational data mining: A survey and a data mining-based analysis of recent works , 2014 .

[13]  Konstantina Chrysafiadi,et al.  Student modeling approaches: A literature review for the last decade , 2013, Expert Syst. Appl..

[14]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[15]  Toshiro Minami,et al.  Toward Learning Support for Decision Making: Utilization of Library and Lecture Data , 2012 .

[16]  H. Finner.  On a Monotonicity Problem in Step-Down Multiple Test Procedures , 1993 .

[17]  Francisco Herrera,et al.  SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification , 2015, IEEE Transactions on Cybernetics.

[18]  David Yarowsky,et al.  Unsupervised Word Sense Disambiguation Rivaling Supervised Methods , 1995, ACL.

[19]  Maria Virvou,et al.  An intelligent recommender system for trainers and trainees in a collaborative learning environment for UML , 2012, Intell. Decis. Technol..

[20]  Francisco Herrera,et al.  On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification , 2014, Neurocomputing.

[21]  Norlida Buniyamin,et al.  An overview of using academic analytics to predict and improve students' achievement: A proposed proactive intelligent intervention , 2013, 2013 IEEE 5th Conference on Engineering Education (ICEED).

[22]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[23]  Panayiotis E. Pintelas,et al.  DSS-PSP - A Decision Support Software for Evaluating Students' Performance , 2017, EANN.

[24]  Jacob D. Furst,et al.  Semi-supervised learning approaches for predicting semantic characteristics of lung nodules , 2009, Intell. Decis. Technol..

[25]  Mihaela van der Schaar,et al.  A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs , 2017, IEEE Journal of Selected Topics in Signal Processing.

[26]  Nawal Ali Yassein,et al.  Predicting Student Academic Performance in KSA using Data Mining Techniques , 2017 .

[27]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[28]  Zhi-Hua Zhou,et al.  SETRED: Self-training with Editing , 2005, PAKDD.

[29]  Zhi-Hua Zhou,et al.  Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.

[30]  Francisco Herrera,et al.  Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study , 2015, Knowledge and Information Systems.

[31]  Nate Johnson.  Three Policies to Reduce Time to Degree. , 2011 .

[32]  Yan Zhou,et al.  Democratic co-learning , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[33]  Nikos Karacapilidis,et al.  Prediction of Students' Graduation Time Using a Two-Level Classification Algorithm , 2018, TECH-EDU.

[34]  Parag Kulkarni,et al.  A Survey of Semi-Supervised Learning Methods , 2008, 2008 International Conference on Computational Intelligence and Security.

[35]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[36]  Juan Alfonso Lara,et al.  Data mining for modeling students' performance: A tutoring action plan to prevent academic dropout , 2017, Comput. Electr. Eng..

[37]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[38]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[39]  Zhongsheng Hua,et al.  Semi-supervised learning based on nearest neighbor rule and cut edges , 2010, Knowl. Based Syst..

[40]  Zhi-Hua Zhou,et al.  Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.