NDLI: Induction algorithm based on statistics theory with Delphi

Content Provider	IEEE Xplore Digital Library
Author	Guo-Gang Li Yan Li Yue-Hua Ren
Copyright Year	2008
Description	Author affiliation: Coll. of Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang (Guo-Gang Li; Yan Li)
Abstract	Analysis by way of the experiment, compared with ID3 algorithm, there is a large difference between SD-CA algorithm in this thesis and decision tree algorithm originated from ID3 And the classification rule is also different from the practice. It relates to the data containing middling, the proportions are all 0.5. Then, its results to classification are much more related to other attributes; some attributes' values have the determinative effect. For instance, only if dressing index is in the attribute of normal, it must be positive. Of course, the others are of different effects. So we'd better think over entirely before come to the final result. According to the statistic probability, while the training set is increasing much more, the proportion of attribute's positive and negative will be stable and the precise of the classification will be higher.
Starting Page	799
Ending Page	804
File Size	296754
Page Count	6
File Format	PDF
ISBN	9781424422388
DOI	10.1109/ICWAPR.2008.4635886
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Training Noise Humidity Accuracy rate Second learning Classification algorithms Decision trees Indexes ID3 algorithm Decision tree Classification tree analysis
Content Type	Text
Resource Type	Article

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