Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool
By Paulo Cortez. 2010
259 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sérgio Moro
- Paulo Rita
- Giuseppe Casalicchio
- Mark J. Embrechts
- Bernd Bischl
- Bernardo Vala
- Raul M. S. Laureano
- Paulo Cortez
In Information Systems
- Sérgio Moro
- Mark J. Embrechts
- Raul M. S. Laureano
- Paulo Rita
- Giuseppe Casalicchio
- Bernd Bischl
- Paulo Cortez
- Bernardo Vala
Citations in the corpus
- Opening black box Data Mining models using Sensitivity Analysis (2011, methodology): “All experiments reported here were conducted using the rminer library and R tool [21], under a linux server.”
- A data-driven approach to predict the success of bank telemarketing (2014, methodology): “For setting these values, rminer uses grid search and heuristics [5].”
- Using sensitivity analysis and visualization techniques to open black box data mining models (2013, methodology): “, function Importance) and the R tool [6].”
- Sport Brands: Brand Relationships and Consumer Behavior (2020, unknown): “Researchers should examine actual behavioral data through panel methods, time series, or data mining, such as regression-based models, decision tree models, dependency models, or neural network models (Cortez, 2010; Fayyad, Piatstsky-Shap-iro, & Smyth, 1996).”
- Using data mining for bank direct marketing: an application of the CRISP-DM methodology (2011, methodology): “All experiments reported in this work were conducted using the rminer library (Cortez 2010), which is an open source package for the R tool that facilitates the use of DM techniques (and in particular, classification tasks), and rattle, a graphical user interface for DM in R (Williams, 2009).”
- Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach (2016, methodology): “Several data mining techniques can be used to model numeric variables, such as linear regression, support vectormachines, and neural networks (Cortez, 2010).”
- mlr: Machine Learning in R (2016, methodology): “…provide frameworks for handling prediction models, including caret (Kuhn, 2008), DMwR (Torgo, 2010), CORElearn (Robnik-Sikonja and with contributions from John Adeyanju Alao, 2016), rattle (Williams, 2011), rminer (Cortez, 2010), CMA (Slawski et al., 2008), and ipred (Peters and Hothorn, 2015).”