Mohammad Mohiuddin Azad

Study of greedy and dynamic programming algorithms for construction of exact and approximate decision trees, rules and reducts for decision tables with many-valued decisions
Analysis and design of classifiers based on decision trees, reducts, and decision rule systems
Data mining, Machine Learning

Mohammad Azad, Igor Chikalov, Shahid Hussain and Mikhail Moshkov. “Restricted multi-pruning of decision trees”. MLDAS (Machine Learning and Data Analytics Symposium) 2016 organized by QCRI (Qatar Computing Research Institute) and Boeing International, Doha, Qatar (best contributed paper).
Mohammad Azad, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov. Multi-pruning of decision trees for knowledge representation and classification. The 3rd IAPR Asian Conference on Pattern Recognition (ACPR), IEEE, pages 604 - 608, 2015.
Mohammad Azad and Mikhail Moshkov. Minimization of decision tree average depth for decision tables with many-valued decisions. Procedia Computer Science, 35:368 - 377, 2014.
Mohammad Azad, Igor Chikalov, and Mikhail Moshkov. Three approaches to deal with inconsistent decision tables -- comparison of decision tree complexity. In 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2013), pages 46 - 54, Springer, 2013.
Mohammad Azad, Igor Chikalov, Mikhail Moshkov, Beata Zielosko. Greedy algorithm for construction of decision trees for tables with many-valued decisions. In Louchka Popova-Zeugmann, editor, Proceedings of the 21th International Workshop on Concurrency, Specification and Programming, Berlin, Germany, September 26-28, 2012.