1998 Fiscal Year Final Research Report Summary
A Study on Symbolic Data Analysis.
| Project/Area Number |
09680378
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Tokyo Denki University |
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Principal Investigator |
ICHINO Manabu 東京電機大学, 理工学部, 教授 (40057245)
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| Project Period (FY) |
1997 – 1998
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| Keywords | pattern recognition / data mining / symbolic data / feature selection / feature evaluation / interclass mutual neighborhood graph / mutual neighborhood graph / geometrical thickness |
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| Research Abstract |
A main theme in the term 1997-1998 is to establish the feature selection algorithm in classification problems. In our desired feature selection method, we have to evaluate a given feature subset simultaneously from the two viewpoints : the effectiveness of class discrimination ; and the effectiveness of the generality for class descriptions. For this purpose, we introduced a new graph concept so called the Interclass Mutual Neighborhood Graph (IMNG) and we constructed several new feature selection algorithms.
Another research theme is to establish a feature selection method which is able to detect "geometrically thin structure in global sense" imbedded in multidimensional symbolic data In this research term, we found a sufficiently effective feature selection method which is based on a simple and intuitively clear principle that "If the given symbolic data has a functional structure, then the data has a geometrically thin structure". This method is useful and powerful as a preprocessing tool for functional identification problems by neural networks. A part of our results was reported in the international conference held at Luxembourg (KESDA-98) and was remarked as a new powerful engine for data mining.
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Research Products
(8 results)
