Development of a Novel Machine Learning Algorithm
for High Dimensional Problems
Supervisors:
1. Dr. Melanie Ooi
Email: [email protected]
Research Profile
Google Scholar Citation
2. Dr. Kuang Ye Chow
Email: [email protected]
Research Profile
Full Research Scholarship with allowance:
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Full-time Master of Engineering Science with ability to convert to a PhD if good progress is demonstrated
and peer-reviewed within the first 12 months. Candidate can undergo a conversion examination to PhD,
whereby if successful, may convert his/her research into a PhD level one.
Full tuition fee waiver.
Monthly allowance:
o RM 1800 for Master of Engineering Science candidate
o If candidate converts successfully to PhD after Year 1, allowance will be RM2000 for Year 2 and
RM2200 for Year 3 of study
Background:
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Alternating Decision Tree (ADTree) is a special class of machine learning algorithm that combines the
benefits of decision trees with boosting.
The greatest benefit of the ADtree is its comprehensibility, whereby the classification decision can be
explicitly understood by humans
The original ADTree implements the simplest form of weak classifier via thresholding which is insufficient for
non-linear high dimensional problems.
Research Aim:
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To extend the ADTree algorithm by using different boosting and tree induction methods, and combining
them with new optimisation techniques suited for high dimensional problems