Intelligent Soft Computation and Evolving Data Mining: by Leon Shyue-Liang Wang, Tzung-Pei Hong

By Leon Shyue-Liang Wang, Tzung-Pei Hong

Because the functions of knowledge mining, the non-trivial extraction of implicit details in a knowledge set, have increased lately, so has the necessity for suggestions which are tolerable to imprecision, uncertainty, and approximation. clever smooth Computation and Evolving info Mining: Integrating complex applied sciences is a compendium that addresses this want. It integrates contrasting innovations of traditional tough computing and gentle computing to use the tolerance for imprecision, uncertainty, partial fact, and approximation to accomplish tractability, robustness and inexpensive resolution. This ebook presents a connection with researchers, practitioners, and scholars in either gentle computing and information mining groups, forming a origin for the improvement of the sphere.

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Part B, 33(2), 324–331. , & Herrera, F. (2006). Hybrid learning models to get the interpretability-accuracy trade-off in fuzzy modeling. Soft Computing, 10(9), 717–734. , & Garrell, J. (2003). Accuracy-based learning classifier systems: models, analysis and applications to classification tasks. Evolutionary Computation, 1(3), 209–238. , & Herrera, F. (2007). Local identification of prototypes for genetic learning of accurate tsk fuzzy rule-based systems. International Journal of Intelligent Systems, 22(9), 909–941.

Import data to KEEL format: Since KEEL works with a specific data format (alike the ARFF format) in all its modules, this section allows us to convert various data formats to KEEL format, such as CSV, XML, ARFF, extracting data from data bases, etc. Export data from KEEL format: This is the opposite option to the previous one. It converts the data handled by KEEL procedures in other external formats to establish compatibility with other software tools. Visualization of data: This option is used to represent and visualize the data.

2000). Fuzzy random variables-based modeling with ga-p algorithms. In B. Bouchon, R. Yager, & L. ), Information, uncertainty and fusion (pp. 245-256). Norwell, MA: Kluwer Academic Publishers. , & Couso, I. (2007). Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE transactions on Fuzzy Systems, 15(4), 551–562. , & Corrales, J. A. (2001). Combining gp operators with sa search to evolve fuzzy rule based classifiers. Information Sciences, 136(1-4), 175–191. , & Otero, J.

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