TY - JOUR AU - Asif, R. AU - Merceron, A. AU - Ali, S. A. AU - Haider, N. G. PY - 2017 DA - 2017// TI - Analyzing undergraduate students’ performance using educational data mining JO - Computers in Education VL - 113 UR - https://doi.org/10.1016/j.compedu.2017.05.007 DO - 10.1016/j.compedu.2017.05.007 ID - Asif2017 ER - TY - CHAP AU - Brodersen, K. H. AU - Ong, C. S. AU - Stephan, K. E. AU - Buhmann, J. M. PY - 2010 DA - 2010// TI - The balanced accuracy and its posterior distribution BT - Proceedings - international conference on pattern recognition ID - Brodersen2010 ER - TY - STD TI - Byrt, T., Bishop, J., & Carlin (1990). Prevalence adjusted bias. Journal of Clinical Epidemiology. ID - ref3 ER - TY - CHAP AU - Comendador, B. E. V. AU - Rabago, L. W. AU - Tanguilig, B. T. PY - 2016 DA - 2016// TI - An educational model based on knowledge discovery in databases (KDD) to predict learner’s behavior using classification techniques BT - 2016 IEEE Int. Conf. Signal process. Commun. Comput ID - Comendador2016 ER - TY - STD TI - Dey, L., Chakraborty, S., Biswas, A., Bose, B., & Tiwari, S. (2016). Sentiment analysis of review datasets using Naïve Bayes‘ and K-NN classifier. Int. J. Inf. Eng. Electron. Bus. ID - ref5 ER - TY - CHAP AU - Fleiss, J. L. AU - Paik, M. C. PY - 2003 DA - 2003// TI - The measurement of interrater agreement, in statistical methods for rates and proportions BT - Statistical methods for rates and proportions UR - https://doi.org/10.1002/0471445428 DO - 10.1002/0471445428 ID - Fleiss2003 ER - TY - STD TI - Ingrassia, S., & Morlini, I. (2005). Neural network modeling for small datasets. Technometrics. ID - ref7 ER - TY - STD TI - Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software. ID - ref8 ER - TY - CHAP AU - Litman, D. J. AU - Forbes-Riley, K. PY - 2004 DA - 2004// TI - Predicting student emotions in computer-human tutoring dialogues BT - Proceedings of the 42nd annual meeting on Association for Computational Linguistics - ACL ‘04 ID - Litman2004 ER - TY - STD TI - Mchugh, M. L. (2012). Interrater reliability: The kappa statistic importance of measuring interrater reliability theoretical issues in measurement of rater reliability. Biochem Med (Zagreb). ID - ref10 ER - TY - JOUR AU - Mueen, A. AU - Zafar, B. AU - Manzoor, U. PY - 2016 DA - 2016// TI - Modeling and predicting students’ academic performance using data mining techniques JO - Int. J. Mod. Educ. Comput. Sci. VL - 8 UR - https://doi.org/10.5815/ijmecs.2016.11.05 DO - 10.5815/ijmecs.2016.11.05 ID - Mueen2016 ER - TY - STD TI - Mustafa, M. K., Allen, T., & Appiah, K. (2017). A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition. Neural Computing and Applications. ID - ref12 ER - TY - STD TI - Pasini, A. (2015). Artificial neural networks for small dataset analysis. Journal of Thoracic Disease. ID - ref13 ER - TY - STD TI - Qiao, Z., Zhou, L., & Huang, J. Z. (2009). Sparse linear discriminant analysis with applications to high dimensional low sample size data. International Journal of Applied Mathematics. ID - ref14 ER - TY - CHAP AU - Rao, R. B. AU - Fung, G. AU - Rosales, R. PY - 2008 DA - 2008// TI - On the dangers of cross-validation. An experimental evaluation BT - Proceedings of the 2008 SIAM international conference on data mining ID - Rao2008 ER - TY - STD TI - Rotich, N. K., Backman, J., Linnanen, L., & Daniil, P. (2014). Wind resource assessment and forecast planning with neural networks. Journal of Sustainable Development of Energy, Water and Environment Systems. ID - ref16 ER - TY - STD TI - Sharma, A., & Paliwal, K. K. (2015). Linear discriminant analysis for the small sample size problem: An overview. International Journal of Machine Learning and Cybernetics.Sharma, A. and Paliwal, K. K., “Linear discriminant analysis for the small sample size problem: An overview,” International Journal of Machine Learning and Cybernetics, 2015. ID - ref17 ER -