0485363 20240103215436.420180123235959.9 85040323043 000425565300007 10.1016/j.ins.2017.12.029 CF-integrals: A new family of pre-aggregation functions with application to fuzzy rule-based classification systems 17 s. P cav_un_epca*02567520020-0255Information Sciences4351 (2018)94-110Elsevier CF-integral classification problems pre-aggregation cav_un_auth*0330393 15 Lucca G. ES K cav_un_auth*0357025 15 Sanz A. ES cav_un_auth*0330395 15 Dimuro G. P. BR cav_un_auth*0298830 15 Bedregal B. BR cav_un_auth*0271524 15 Bustince H. ES cav_un_auth*0101163 Ekonometrie Department of Econometrics E E Department of Econometrics 25 Mesiar Radko UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2018/E/mesiar-0485363.pdf This paper introduces the family of CF-integrals, which are pre-aggregations functions that generalizes the Choquet integral considering a bivariate function F that is left 0-absorbent. We show that CF-integrals are 1->-pre-aggregation functions, studying in which conditions they are idempotent and/or averaging functions. This characterization is an important issue of our approach, since we apply these functions in the Fuzzy Reasoning Method (FRM) of a fuzzy rule-based classification system and, in the literature, it is possible to observe that non-averaging aggregation functions usually provide better results. We carry out a study with several subfamilies of CF-integrals having averaging or non-averaging characteristics. As expected, the proposed non-averaging CF-integrals obtain more accurate results than the averaging ones, thus, offering new possibilities for aggregating accurately the information in the FRM. Furthermore, it allows us to enhance the results of classical FRMs like the winning rule and the additive combination. BA 10000 10100 10101 2019 6 RVO:67985556 http://hdl.handle.net/11104/0281414 S 1* Article Computer Science Information Systems COMPUTERSCIENCE.INFORMATIONSYSTEMS 5.305 1.537 7.3 0.0508 1.111 31734 1.620 4.784 748 Q1 86.641 94.5 Q1 Q1 2.19 Q1 94.516 2018 85040323043 SCOPUS PUBMED 000425565300007 WOS cav_un_epca*0256752 Information Sciences 0020-0255 1872-6291 Roč. 435 č. 1 2018 94 110 Elsevier