jakramate_pic

Jakramate Bootkrajang


Lecturer, Department of Computer Science
Chiang Mai University
Muang, Chiang Mai
50200, Thailand
jakramate.b (at) cmu.ac.th


Research Interests
Statistical Machine Learning, Learning under uncertainty, Probabilistic Modelling.

Publications
2017
K. Lehsan, J. Bootkrajang, : Predicting Physical Activities from Accelerometer Readings in Spherical Coordinate System, IDEAL 2017 (accepted)
2016
P. Inkeaw, J. Bootkrajang, P. Charoenkwan, S. Marukatat, S.Y. Ho, and J. Chaijaruwanich: Rule-Based Page Segmentation for Palm Leaf Manuscript on Color Image, ICADL 2016
J. Bootkrajang: A Generalised Label Noise Model for Classification in the Presence of Annotation Errors, Neurocomputing
2015
A. Kaban, J. Bootkrajang, R.J. Durrant. Towards Large Scale Continuous EDA: A Random Matrix Theory Perspective. Evolutionary Computation, Evolutionary Computation 24(2): 255-291, 2016, MIT Press
J. Bootkrajang: A Generalised Label Noise Model for Classification, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), pp 349-354.
2014
J. Bootkrajang, A. Kaban: Learning Kernel Logistic Regression in the Presence of Class Label Noise, Pattern Recognition, Vol. 47, Issue 11, November 2014, pp.3641-3655
2013
J. Bootkrajang, A. Kaban: Boosting in the Presence of Label Noise, Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013)
A. Kaban, J. Bootkrajang, R.J. Durrant: Towards Larger Scale Continuous EDA: A Random Matrix Theory Perspective, 22nd International Conference on Genetic Algorithms and 18th Annual Genetic Programming Conference (GECCO 2013), 6-10 July 2013, Amsterdam, The Netherlands. Best Paper Award in the GDS/EDA Track
J. Bootkrajang, A. Kaban: Classification of Mislabelled Microarrays using Robust Sparse Logistic Regression, Bioinformatics 29(7): 870-877, 2013.
J. Bootkrajang, A. Kaban: Learning a Label-noise Robust Logistic Regression: Analysis and Experiments, The international Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2013). 20-23 October 2013, Hefei, China.
2012
J. Bootkrajang, A. Kaban: Label-noise Robust Logistic Regression and its Applications, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2012), 24-28 September 2012, Bristol, UK.
2011
J. Bootkrajang, A. Kaban: Multi-class Classification in the Presence of Labelling Errors, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2011), 23-29 April 2011, Bruges, Belgium.
2010
H.-S. Seok, J. Bootkrajang, B.-T. Zhang: Hypernetwork-based Natural Language Sentence Generation by Word Relation Pattern Learning, Journal of the Korea Information Science Society: Software and Applications, 37(3):205-213, 2010. (Extended journal version)
H.-S. Seok, J. Bootkrajang, B.-T. Zhang: Hypernetwork-based Natural Language Sentence Generation by Word Relation Pattern Learning, KCC 2010. (in Korean)
D. Punithan, J. Marhic, K. Kim, J. Bootkrajang, R. I. (Bob) McKay, N. Mori: An XML Format for Sharing Evolutionary Algorithm Output and Analysis, SEAL 2010.
2009
J. Bootkrajang, S. Kim, B.-T. Zhang: Evolutionary Hypernetwork Classifiers for Protein-Protein Interaction Sentence Filtering, International Conference on Genetic Algorithms and 18th Annual Genetic Programming Conference (GECCO 2009), Montreal, Canada.
2008
J. Bootkrajang, S. Kim, B.-T. Zhang: Text Sentence Classification using Hypernetwork Models, IEEK, 2008. (in Korean)

PhD Thesis
J. Bootkrajang: Supervised Learning with Random Labelling Errors, University of Birmingham, 2013. (Supervisor Dr. Ata Kaban)


Teaching
1/2017
204381 Numerical computation and software
204423 Data Mining
2/2016
204789 Machine Learning and Neural Network
204202 Information Technology 2
204490 Research in Computer Science
215433 Bioinformatics
1/2016
204101 Introduction to Computer
204423 Data Mining
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Final year and MSc projects
Classification: Develop a system to classify items, e.g, image data, biomedical data.
Recommender system: Develop a system to recommend products, travel destinations or foods based on user's history.
Image analysis: Image segmentation, face detection, etc.
Note: The main aim of the project is to bring cool stuffs found in research papers to real-world usage.

Scholarly
Journal reviewing
Information Sciences
Transactions on Neural Networks and Learning Systems (IEEE)
Neurocomputing
Conferences and Workshops reviewing
Genetic and Evolutionary Computation Conference (GECCO 2012)
Genetic and Evolutionary Computation Conference (GECCO 2011)
Special session on Label Noise in Classification at ESANN 2014
Program Committee
Special Session on Combining Learning and Optimisation for Intelligent Data Engineering at IDEAL 2013
Genetic and Evolutionary Computation Conference (GECCO 2012)

Software
Codes
Generalised Label Noise Model (ESANN 2015 & Neurocomputing 2016)
RP-EDA (Evolutionary Computation 2015)
Robust boosting (UAI 2013)
Sparse Robust Logistic Regression (Bioinformatics 2013)
Robust Logistic Regression (ECML 2012)
MATLAB implementation
JULIA implementation (via Github)
Dataset
Websearch (ECML 2012)

Misc.
LaTeX template
CS-CMU Handout template [TeX] [PDF]
Programming assignment template [TeX] [PDF]

Last modified: 3 March 2017

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