Data mining - practical machine learning tools and techniques, Second Edition

By Ian H. Witten, Eibe Frank. The Morgan Kaufmann series in data management systems, 2005

2,600 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/49d1f40312af25d01da721e33f4a8be8cea29551

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Citations in the corpus

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 Classification using Graph Mining-based Feature Extraction (2010, methodology): “For example, using PCC weighting with support 10%, the accuracy of NBC on D2 is 76.5% and the accuracy of NBC on D3 is 72.3%.”
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017, result): “Like WEKA (Witten and Frank 2005), a popular collection of machine learning algorithms, MOA is also mainly developed by the University of Waikato and its graphical user interface (GUI) and workflow are similar to those of WEKA.”
Efficient Multi-Start Strategies for Local Search Algorithms (2009, methodology): “…of a learning algorithm, we have used two standard data sets from the UCI Machine Learning Repository (Asuncion & Newman, 2007): vehicle and letter, and the Multilayer Perceptron learning algorithm of Weka (Witten & Frank, 2005) (here the back-propagation algorithm is used in the training phase).”
AnalogySpace: Reducing the Dimensionality of Common Sense Knowledge (2008, methodology): “For example, the goal of the WAC project (Coursey 2007) is to let Cyc discover patterns in its own data using the Weka (Witten & Frank 2005) classifier.”
Recognising Human Emotions from Body Movement and Gesture Dynamics (2007, methodology): “We used Weka [20], an open source suite of machine learning tools for data mining, as the basis for classification.”
A data-driven approach to predict the success of bank telemarketing (2014, result): “For the feature selection, we adopted the NN model described in Section 2.2 as the base DM model, since preliminary experiments, using only training data, conﬁrmed that NN provided the best AUC and ALIFT results when compared with other DM methods.”
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter (2011, methodology): “Using the classification setup described above and these feature groups, we tested 30 classification algorithms using the Weka machine learning toolkit (Witten and Frank 2005).”
Testing and Validating Machine Learning Classifiers by Metamorphic Testing (2011, unknown): “To demonstrate the effectiveness of metamorphic testing in validating machine learning applications, we applied the approach to Weka 3.5.7 (Witten and Frank, 2005).”
Text mining for market prediction: A systematic review (2014, methodology): “Schumaker et al. (2012) choose to implement the SVR Sequential Minimal Optimization (Platt, 1999) function through Weka (Witten & Frank, 2005).”
Classifying Software Changes: Clean or Buggy? (2008, methodology): “The10-fold cross-validation technique [35, 52] is used to handle this problem in our experiment.”
Using mobile phones to determine transportation modes (2010, methodology): “The testing setup has even numbers of every class so the overall accuracy is simply the average of the recall values for each of the transportation modes [Witten and Frank 2005].”
CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites (2011, methodology): “All the ML algorithm implementations were taken from the Weka package [Witten and Frank 2005].”
SemiBoost: Boosting for Semi-Supervised Learning (2009, methodology): “Software WEKA [33] was used to implement all the three classifiers.”