Automatic Clustering Based Feature Combination for Latent Topic Based Classification
碩士 === 真理大學 === 資訊工程學系碩士班 === 100 === In this work, we demonstrate a feature combination process based on automatic clustering. This procedure delivers a better classification result than the traditional classification models. In our experiment, we detect the best cluster number for each feature bas...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/28799598940793397505 |
| Summary: | 碩士 === 真理大學 === 資訊工程學系碩士班 === 100 === In this work, we demonstrate a feature combination process based on automatic clustering. This procedure delivers a better classification result than the traditional classification models. In our experiment, we detect the best cluster number for each feature based on the distribution off feature data with a specially designed cluster number decision formula called HCV function. This function estimates the best intra- and inter- cluster relationship with a harmonic based mean calculation. With HCV, we successfully decrease the number of parameters needed in our feature combination process in respect to our previous method. According to the experiment, we found a better ROC performance than traditional classifier and other configurations with our design.
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