( en | de | ru )

 

Disambiguation of "Corrales, David Camilo"

Author of the publication

copydeleteadd this publication to your clipboard
Sequential classifiers for network intrusion detection based on data selection process.

, , , and . SMC, page 1827-1832. IEEE, (2016)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Camilo Gerhard

Tomas Corrales

Camilo Michalka

Camilo Pérez

Mercedes Corrales Carvajal

 

Other publications of authors with the same name

Rule-based expert system for detection of coffee rust warnings in colombian crops., , , , and . J. Intell. Fuzzy Syst., 36 (5): 4765-4775 (2019)Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches., , , , and . Comput. Electron. Agric., (2020)A case-based reasoning system for recommendation of data cleaning algorithms in classification and regression tasks., , and . Appl. Soft Comput., (2020)How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning., , and . Symmetry, 10 (4): 99 (2018)Lack of Data: Is It Enough Estimating the Coffee Rust with Meteorological Time Series?, , , , and . ICCSA (2), volume 10405 of Lecture Notes in Computer Science, page 3-16. Springer, (2017)Volcanic Anomalies Detection Through Recursive Density Estimation., , , , and . MICAI (1), volume 11288 of Lecture Notes in Computer Science, page 299-314. Springer, (2018)Feature selection for classification tasks: Expert knowledge or traditional methods?, , , and . J. Intell. Fuzzy Syst., 34 (5): 2825-2835 (2018)An Empirical Multi-classifier for Coffee Rust Detection in Colombian Crops., , , and . ICCSA (1), volume 9155 of Lecture Notes in Computer Science, page 60-74. Springer, (2015)Sequential classifiers for network intrusion detection based on data selection process., , , and . SMC, page 1827-1832. IEEE, (2016)A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France., , , , , and . Comput. Electron. Agric., (2022)