2020 Fiscal Year Research-status Report
Development of Fast and Highly Effective Feature Subset Selection Algorithms based on Novel Integration of Quantum Computing and Machine Learning
| Project/Area Number |
20K11939
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| Research Institution | Iwate Prefectural University |
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Principal Investigator |
チャクラボルティ バサビ 岩手県立大学, ソフトウェア情報学部, 教授 (90305293)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | feature selection / metaheuristic algorithm / quantum inspired / optimal feature subset |
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| Outline of Annual Research Achievements |
We have developed a new metaheuristic based computationally simple algorithm (Binary owl Serach Algorithm) for optimum feature subset selection. In the next stage, we modified the algorithm by introducing self adaptivity, elitism and mutation operation to strengthen the searching capability of the algorithm for getting better solution. Finally we proposed another extension of the Binary Owl Search algorithm, a quantum inspired binary owl search algorithm, to improve computational time. We have also refined the QUBO formulation of filter function to develop quantum computing based algorithm. We have published two international conference papers and two journal papers. This year we intend to work towards developing a general framework of quantum machine learning.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We could achieve what we planned for the first year of the project. Communication with other members of my team went quite well.
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| Strategy for Future Research Activity |
Now we are working for evaluation of our proposed algorithms for larger data sets and high dimensional data sets. We are also planning to extend our work for defining a general framework of integration of machine learning with quantum computing.
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| Causes of Carryover |
Due to pandemic problem, the plan for this year was not fulfilled.
The plan for the next year:
1) Buy GPU machine for simulation with high dimensional data.
2)International conference attandance, if possible.
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