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 court cases of the 4th overseas convention on clever information research, IDA 2001, held in Cascais, Portugal, in September 2001.
The 37 revised complete papers offered have been conscientiously reviewed and chosen from a complete of virtually a hundred and fifty submissions. All present facets of this interdisciplinary box are addressed; the components lined contain facts, man made intelligence, neural networks, computer studying, facts mining, and interactive dynamic info visualization.
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Additional info for Advances in Intelligent Data Analysis: 4th International Conference, IDA 2001 Cascais, Portugal, September 13–15, 2001 Proceedings
Table 2 shows the results of the experiments about query expansion with CACM. 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).
Vila, Data Summarization with Linguistic Labels: A Loss Less Decomposition Approach, in Proc. of IFSA, 1997. 5. D. Dubois and H. Prade, Fuzzy sets in data summaries - Outline of a new approach, in Proc. of IPMU, Granada (Spain), pp 1035-1040, 2000. 6. J. R. Yager and S. Zadrozny, A fuzzy logic based approach to linguistic summaries of databases, in J. of Applied Mathematics and Computer Science, 2000. 7. M. Kamber, J. Han and J. Y. Chiang, Metarule-Guided Mining of MultiDimensional Association Rules, in KDD, 1997.
In other words, precision corresponds to the proportion of examples classiﬁed as positive that are truly positive; recall corresponds to the proportion of truly positive examples that are classiﬁed as positive; the FB -measure combines the precision and recall by a ratio speciﬁed by B. If B = 1, then precision and recall are considered as being of equal importance. If B = 2, then recall is considered to be twice as important as precision. 5, then precision is considered to be twice as important as recall.