Weka: Practical machine learning tools and techniques with Java implementations

By Ian H. Witten, Eibe Frank, Leonard E. Trigg, Mark A. Hall, G. Holmes, S. Cunningham. 1999

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In ML Systems & Hardware

Citations in the corpus

From microbial sprays to insect-resistant transgenic plants: history of the biospesticide Bacillus thuringiensis. A review (2011, unknown): “• 1997 Λήφθηκε η απόφαση να ανακατασκευαστεί το Weka από την αρχή σε Java, συμπεριλαμβανομένων των εφαρμογών του διαμόρφωσης αλγορίθμων [13]”
Diagnosis of diabetes using classification mining techniques (2015, methodology): “Pre-processing and transformation of the dataset are done using WEKA tools [11].”
AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems (2022, methodology): “Several popular classification techniques such as decision trees [72], IntrudTree [82], BehavDT [84], Ripple Down Rule learner (RIDOR) [101], Repeated Incremental Pruning to Produce Error Reduction (RIPPER) [102], etc. exist with the ability of rule generation.”
SoundSense: scalable sound sensing for people-centric applications on mobile phones (2009, methodology): “48 algorithm [43] as it is simple to construct and execute while being equivalent in performance to the alternatives.”
Recognizing Textual Entailment Using Sentence Similarity based on Dependency Tree Skeletons (2007, methodology): “For the kernel-based classification, we used the classifier SMO from the WEKA toolkit (Witten and Frank, 1999).”
AutoBI - a tool for automatic toBI annotation (2010, methodology): “The detection and classiﬁcation of prosodic events is accomplished using classiﬁers trained using the weka machine learning toolkit [9].”
Challenges in real-life emotion annotation and machine learning based detection (2005, unknown): “For the paralinguistic model, we use the Weka machine learning software (Witten et al., 1999).”
Robust prediction of fault-proneness by random forests (2004, result): “Three data sets in our case studies demonstrate that similar 80/20 rule holds true for static artifacts, code defects (see Figure 1).”