The WEKA data mining software: an update

By Mark A. Hall, Eibe Frank, G. Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten. 2009

20,738 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Evaluation & Benchmarks, ML Systems & Hardware, Signal Processing

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Citations in the corpus

Learning Optimal Decision Trees with SAT (2018, methodology): “Furthermore, most ML and DM packages implement some algorithm for learning decision trees [Hall et al. , 2009; Pedregosa and et al., 2011].”
Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, methodology): “We also compare our results with the results produced with different configurations of the Random Forest (RF) algorithm implemented in Weka (Hall et al., 2009) (RF(T=1,. .”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “For all baseline methods, we used the Weka [27] software package on a Mac-Book Pro with”
Machine Learning that Matters (2012, methodology): “There are also problems with how we measure performance.”
Doubly Robust Policy Evaluation and Learning (2011, methodology): “Its input is of the same form as for Direct Loss Minimization, but its output is a binary-tree based predictor where each node of the Filter Tree uses a binary classiﬁer—in this case the J48 decision tree implemented in Weka 3.6.4 (Hall et al., 2009).”
Initializing Bayesian Hyperparameter Optimization via Meta-Learning (2015, methodology): “performs combined algorithm selection and hyperparameter optimization in the space of algorithms defined by the WEKA package (Hall et al. 2009).”
UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment (2014, methodology): “Next, two machine learning models were obtained (WEKA (Hall et al., 2009) was used for that) using the training part of SICK, one for regression (relatedness) and another for classiﬁ-cation (entailment).”
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn (2014, background): “The Auto-Weka project [Tho13] was the first to show that an entire library of machine learning approaches (Weka [Hal09] ) can be searched within the scope of a single run of hyperparameter tuning.”
Fitting Sentence Level Translation Evaluation with Many Dense Features (2014, methodology): “For training this model, we use Logistic Regression from the Weka toolkit (Hall et al., 2009).”
Fiji - an Open Source platform for biological image analysis (2012, methodology): “…data, users can employ the Trainable Segmentation plugin, which leverages the Waikato Environment for Knowledge Analysis (WEKA) library for machine learning 40 to extract statistical properties from user-provided examples and apply them to classify the rest of the image or other similar images 41 .”
Evaluation of clustering algorithms for financial risk analysis using MCDM methods (2014, methodology): “6 [29] to get 2-way clustering solutions using k-means, EM, COBWEB, and MakeDensityBasedClusterer algorithms described in Section 2.”
Machine Learning Testing: Survey, Landscapes and Horizons (2019, methodology): “Data are collected and pre-processed for use; the learning program is the code for running to train the model; the framework (e.g., Weka, scikit-learn, and TensorFlow) offers algorithms and other libraries for developers to choose from, when writing the learning program.”
Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection (2010, methodology): “TESTS AND EXPERIMENTAL RESULTS The tests and experiments were conducted using Weka [1] 3.6.2 for Windows OS version.”
A Tutorial on Multilabel Learning (2015, methodology): “[Tsoumakas et al. 2011] is an open-source Java API for multilabel classification that is built on top of Weka [Hall et al. 2009] and implements many transformation methods like BR, LP, copy methods, EPS, or CLR and multilabel algorithms like ML-kNN, RAkEL, HOMER, or BP-MLL.”
Paralinguistics in speech and language - State-of-the-art and the challenge (2013, methodology): “For transparency and easy reproducibility, we used the WEKA data mining toolkit for classification and regression (Witten and Frank, 2005; Hall et al., 2009).”