By Elizabeth Bradley, Nancy Collins, W. Philip Kegelmeyer (auth.), Frank Hoffmann, David J. Hand, Niall Adams, Douglas Fisher, Gabriela Guimaraes (eds.)
This e-book constitutes the refereed complaints of the 4th foreign convention on clever information research, IDA 2001, held in Cascais, Portugal, in September 2001.
The 37 revised complete papers awarded have been conscientiously reviewed and chosen from a complete of virtually one hundred fifty submissions. All present elements of this interdisciplinary box are addressed; the components coated comprise information, man made intelligence, neural networks, computer studying, info mining, and interactive dynamic facts visualization.
Read Online or Download Advances in Intelligent Data Analysis: 4th International Conference, IDA 2001 Cascais, Portugal, September 13–15, 2001 Proceedings PDF
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Extra info for Advances in Intelligent Data Analysis: 4th International Conference, IDA 2001 Cascais, Portugal, September 13–15, 2001 Proceedings
In order to focus on the impact of added terms, the original query will remain unchanged, instantiating all its terms to relevant. The ﬁrst two experiments show that expanding the original query with all the positive expansion terms as being original terms is not a good idea, because these new query terms impair the original query. The third experiment applies the qe1 weighting approach, and the result has been considerably improved because each positive expansion term is added with a λ vector diﬀerent from (0, 1).
Diﬀerent summaries are generated at this level. The user may then specify the summaries and dimensions to explore further. Intra-dimension Summaries. Summaries like “Most middle sales in eastern cities are from Chicago” are called Intra-dimension Summaries. The model proposed here is well adapted to the generation of such summaries since it allows both browsing between granularity levels and ﬂexible queries. As seen previously, the number of possible summaries that may be generated is very high, thus some means to reduce the generation cost are required.
56% over the negative class). 93%. In general, it is quite likely that the optimal sampling rates can vary in a way that might not be predictable for various approaches and problems. 0 sampled at diﬀerent rates. On the one hand, the combination of the oversampling and undersampling strategies may be useful given the fact that the two approaches are both useful in the presence of imbalanced data sets (cf. 6 On the other hand, the combination of classiﬁers using diﬀerent oversampling and undersampling rates may be useful since we may not be able to predict, in advance, which rate is optimal (cf.