The use of machine learning “black boxes” explanation systems to improve the quality of school education

The paper describes development of a multi-criteria decision support system (MCDSS) to improve the quality of school education. It is proposed to apply interpretable machine learning models for mak...

[1]  Vikram Garaniya,et al.  Developing a novel risk-based methodology for multi-criteria decision making in marine renewable energy applications , 2017 .

[2]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[3]  C. Hwang,et al.  TOPSIS for MODM , 1994 .

[4]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[5]  Sunil Pranit Lal,et al.  Educational courseware evaluation using Machine Learning techniques , 2013, 2013 IEEE Conference on e-Learning, e-Management and e-Services.

[6]  Adel Gastli,et al.  PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation , 2011 .

[7]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[8]  Prasanta Kumar Dey,et al.  A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments , 2015 .

[9]  Adil Baykasoglu,et al.  A Hybrid MCDM for Private Primary School Assessment Using DEMATEL Based on ANP and Fuzzy Cognitive Map , 2014, Int. J. Comput. Intell. Syst..

[10]  Edmundas Kazimieras Zavadskas,et al.  Sustainable and Renewable Energy: An Overview of the Application of Multiple Criteria Decision Making Techniques and Approaches , 2015 .

[11]  P. Hansen,et al.  A new method for scoring additive multi‐attribute value models using pairwise rankings of alternatives , 2008 .

[12]  E.N. Ogor.  Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques , 2007, Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007).

[13]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[14]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[15]  Aylin Çiğdem Köne,et al.  An Analytical Network Process (ANP) evaluation of alternative fuels for electricity generation in Turkey , 2007 .

[16]  Minsu Ha,et al.  Assessing Scientific Practices Using Machine-Learning Methods: How Closely Do They Match Clinical Interview Performance? , 2013, Journal of Science Education and Technology.

[17]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[18]  Metin Dağdeviren,et al.  A hybrid MCDM approach to assess the sustainability of students’ preferences for university selection , 2014 .

[19]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[20]  Kirill Yakunin,et al.  Multi-Criteria Spatial Decision Making Supportsystem for Renewable Energy Development in Kazakhstan , 2019, IEEE Access.

[21]  Patrick J. F. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 2003 .

[22]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[23]  P. Vincke,et al.  Note-A Preference Ranking Organisation Method: The PROMETHEE Method for Multiple Criteria Decision-Making , 1985 .