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Developing Anti-spam Filters Using Automatically Generated Rough Sets Rules.

, , , и . IS-MiS, том 171 из Advances in Intelligent Systems and Computing, стр. 325-334. Springer, (2012)

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Improving the drug discovery process by using multiple classifier systems., , , , и . Expert Syst. Appl., (2019)Wirebrush4SPAM: a novel framework for improving efficiency on spam filtering services., , , и . Softw. Pract. Exp., 43 (11): 1299-1318 (2013)Analyzing the Impact of Unbalanced Data on Web Spam Classification., , , , , и . DCAI, том 373 из Advances in Intelligent Systems and Computing, стр. 243-250. Springer, (2015)Model for optimising the execution of anti-spam filters.. Inteligencia Artif., 19 (58): 45-48 (2016)Concept drift in e-mail datasets: An empirical study with practical implications., , и . Inf. Sci., (2018)Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory., , , и . Sci. Program., (2016)An ontology knowledge inspection methodology for quality assessment and continuous improvement., , , и . Data Knowl. Eng., (2021)Using new scheduling heuristics based on resource consumption information for increasing throughput on rule-based spam filtering systems., , , и . Softw. Pract. Exp., 46 (8): 1035-1051 (2016)Improving Pipelining Tools for Pre-processing Data., , , , , и . Int. J. Interact. Multim. Artif. Intell., 7 (4): 214 (2022)Optimising anti-spam filters with evolutionary algorithms., , , и . Expert Syst. Appl., 40 (10): 4010-4021 (2013)