| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Outline of Final Research Achievements |
This research project has developed two practical feature selection algorithms, BornFS and UFVS, with high time efficiency that can scale to bigdata.
BornFS, a feature selection algorithm in the supervised learning context, evaluates relevance, feature count and noise, which is a new measure to evaluate performance of feature selection introduced in our research, and is capable to features with an optimal balance among values of these three measures.
UFVS, a feature selection algorithm in the unsupervised learning context on the other hand, outperforms any known algorithms in the literature in time efficiency. In principle, feature selection under the unsupervised learning setting is known to be significantly difficult, and as a result, the known algorithms were very slow. In contrast, UFVS has time efficiency that can scale to bigdata. In the experiments, UFVS could select small numbers of effective features for datasets with class labels but without using the class labels.
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