YALE: rapid prototyping for complex data mining tasks
By Ingo Mierswa, M. Wurst, Ralf Klinkenberg, Martin Scholz, Timm Euler. Knowledge Discovery and Data Mining, 2006
1,275 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/55f44d39630646f36eac91358f8f27d1bead384c
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
- Francisco Herrera
- Salvador Garcia
- Jimeng Sun
- Bernhard Schölkopf
- Gunnar Rätsch
- M. J. Jesús
- Alberto Fernández
- Yoshua Bengio
In Information Systems
- Sebastián Ventura
- Cristóbal Romero
- Heiko Paulheim
- Ryan Shaun Joazeiro de Baker
- Salvador Garcia
- Kurt Hornik
- David Meyer
- Ingo Feinerer
Citations in the corpus
- FLASH: Fast Bayesian Optimization for Data Analytic Pipelines (2016, methodology): “Many machine learning toolboxes such as scikit-learn [36], RapidMiner [31], SPSS [10], Apache Spark [30] provide mechanisms for configuring analytic pipelines.”
- KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008, background): “Some examples of open source DM systems are Weka [55], Yale [29] and Tanagra [40].”
- A review of recent advances in learner and skill modeling in intelligent learning environments (2012, methodology): “This type of method is not currently explicitly supported in data mining/machine learning packages, but can be achieved using (for instance) Batch Cross-Validation in RapidMiner (Mierswa et al. 2006).”
- A user's guide to support vector machines. (2010, background): “de/), Weka [22], Lush [23], Shogun [24], RapidMiner [25], and PyML (http: //pyml.”
- Unsupervised generation of data mining features from linked open data (2012, background): “For example, a valuable pattern in the book-selling domain could be “Scientific books are sold well in cities with at least one large university” .”
- Latent dirichlet allocation for tag recommendation (2009, methodology): “For mining association rules, we have used RapidMiner [24].”
- The WEKA data mining software: an update (2009, neutral): “The Konstanz Information Miner (KNIME) and RapidMiner [20] are two such systems.”
- Sentiment Classification of Reviews Using SentiWordNet (2009, methodology): “The experim nt was executed using the support vector machine implementation available in the RapidMiner data mining application [11].”
- Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings (2008, methodology): “Therefore, different search techniques (K2, simulated annealing, tabu search, hill climbing, tree augmented Naive Bayes) implemented in the YALE machine learning workbench [45] have been evaluated.”
- Support Vector Machines and Kernels for Computational Biology (2008, background): “The user has a large number of choices, and the following is an incomplete list of environments that provide an SVM classifier: Orange [62], The Spider [63], Elefant [64], Plearn [65], Weka [66], Lush [67], Shogun [68], RapidMiner [69], PyML [70], and Easysvm [17].”
- Text Mining Infrastructure in R (2008, methodology): “Another tools are RapidMiner (formerly Yale (Mierswa et al. 2006)), a system for knowledge discovery and data mining, and Pimiento (Adeva and Calvo 2006), a basic Java framework for text mining.”
- Automatic classifier selection for non-experts (2014, methodology): “We integrated the recommendation tool into RapidMiner [28], that is the most frequently used data mining tool according to the KDNuggets poll from 2010 [31].”
- Data stream clustering: A survey (2013, neutral): “(2) Rapid-Miner [Mierswa et al. 2006].”