Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Armando Segatori"'
Publikováno v:
IEEE Transactions on Fuzzy Systems. 26:174-192
Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose a di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f86a70814aaf8c3b3bf22afeddaf6677
https://doi.org/10.1109/tfuzz.2016.2646746
https://doi.org/10.1109/tfuzz.2016.2646746
Publikováno v:
Expert Systems with Applications. 42:2086-2097
We propose a novel efficient fuzzy associative classification approach.We exploit a fuzzy version of the FP-Growth algorithm.We perform an experimental analysis on 17 classification datasets.We compare our approach with three well-known associative c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b795fa7187fb2972531f359c97f6cfe
https://doi.org/10.1016/j.eswa.2014.09.021
https://doi.org/10.1016/j.eswa.2014.09.021
Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0a6a15afe83d3c22f1561abed54e376
http://hdl.handle.net/11568/940077
http://hdl.handle.net/11568/940077
In the last years, multi-objective evolutionary algorithms (MOEAs) have been extensively used to generate sets of fuzzy rule-based classifiers (FRBCs) with different trade-offs between accuracy and interpretability. Since the computation of the accur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33ecbc78911de3f5054fadc71d9bb79d
http://hdl.handle.net/11568/881484
http://hdl.handle.net/11568/881484
Publikováno v:
SMC
Random forests are currently considered among the most accurate and efficient classifiers. Moreover, recently fuzzy implementations of random forests have been proposed to exploit the ability of fuzzy decision trees to cope with uncertain data. Whene
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e432d8792ec38547a621d196d66cd7c
https://doi.org/10.1109/smc.2016.7844638
https://doi.org/10.1109/smc.2016.7844638
Publikováno v:
FUZZ-IEEE
One of the most appealing features of fuzzy rule-based classifiers is the capability of explaining how the conclusions are inferred. This feature is hard to preserve when fuzzy rules are extracted from a very large amount of data. In this paper, we p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53fdd1e072423e0f9fb799430c8d6519
http://hdl.handle.net/11568/826816
http://hdl.handle.net/11568/826816
Fuzzy rule-based models have been extensively used in regression problems. Besides high accuracy, one of the most appreciated characteristics of these models is their interpretability, which is generally measured in terms of complexity. Complexity is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a0ef61eecb2f93c3f3f7f578f3da932
http://hdl.handle.net/11568/799455
http://hdl.handle.net/11568/799455
Associative classifiers have proven to be very effective in classification problems. Unfortunately, the algorithms used for learning these classifiers are not able to adequately manage big data because of time complexity and memory constraints. To ov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::432dd0d20c8b01251c2858dcafb3fb7a
http://hdl.handle.net/11568/799448
http://hdl.handle.net/11568/799448
Publikováno v:
FUZZ-IEEE
Random forests have proved to be very effective classifiers, which can achieve very high accuracies. Although a number of papers have discussed the use of fuzzy sets for coping with uncertain data in decision tree learning, fuzzy random forests have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e770dd3b52d1e423691c0de4d82a59a8
https://doi.org/10.1109/fuzz-ieee.2015.7337919
https://doi.org/10.1109/fuzz-ieee.2015.7337919
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783319039916
Advances onto the Internet of Things
Advances onto the Internet of Things
The Internet of Things (IoT) is today considered as one of the most important enabling technologies for developing a wide variety of smart services aimed at assisting the final user in the urban environment. In this chapter, we present how IoT can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dc4a655cb49daf6a3af54a2d8e859a3
https://doi.org/10.1007/978-3-319-03992-3_17
https://doi.org/10.1007/978-3-319-03992-3_17