Orange: From Experimental Machine Learning to Interactive Data Mining

By Jaka Demsar, Blaz Zupan, Gregor Leban, Tomaž Curk. European Conference on Principles of Data Mining and Knowledge Discovery, 2004

522 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/78a907839eadb15730ed51b574009067a5ac394a

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In Computer Vision and Pattern Recognition

Citations in the corpus

Statistical Comparisons of Classifiers over Multiple Data Sets (2006, methodology): “5 code, equipped with an interface that integrates it into machine learning system Orange (Demšar and Zupan, 2004), which provided us with the cross validation procedures, classes for tuning arguments, and the scoring functions.”
Predictive data mining in clinical medicine: Current issues and guidelines (2008, methodology): “The snapshots of visualization of data mining modls and results presented in this section were obtained using he Orange data mining suite [4].”
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020, background): “[18] to describe an approach where people are involved in an iterative process of training, using, and correcting an ML model, requiring interpretability for effective corrections [13].”
Contrast Set Mining for Distinguishing Between Similar Diseases (2007, methodology): “In Section 4 we show an approach to contrast set mining through subgroup discovery by providing a mathematically correct translation from contrast set mining to subgroup discovery [7] and an implementation of the approach in the Orange [2] open source data mining toolbox.”
Pattern for Python (2012, background): “Several such toolkits with a user interface exist in the scientific community, for example ORANGE (Demšar et al., 2004) for machine learning and GEPHI (Bastian et al., 2009) for graph visualization.”
Nomograms for visualizing support vector machines (2005, methodology): “As for earlier nomograms, all experiments were performed within the Orange toolkit [4].”
The Need for Open Source Software in Machine Learning (2007, background): “, 2007), Orange (Demsar and Zupan, 2004), PLearn (Vincent et al.”
Software Framework for Topic Modelling with Large Corpora (2010, methodology): “These include NLTK (Bird and Loper, 2004), Apache's UIMA and ClearTK (Ogren et al., 2008), Weka (Frank et al., 2005), OpenNLP (Baldridge et al., 2002), Mallet (McCallum, 2002), MDP (Zito et al., 2008), Nieme (Maes, 2009), Gate (Cunningham, 2002), Orange (Demšar et al., 2004) and many others.”
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 [65], The Spider [66], Elefant [67], Plearn [68], Weka [69], Lush [70], Shogun [71], RapidMiner [72] PyML [73], and Easysvm [21].”
Independent consultant (2013, unknown): “Orange: From experimental machine learning to interactive data mining.”
Testing and Validating Machine Learning Classifiers by Metamorphic Testing (2011, unknown): “ML frameworks such as Orange (Demsar et al., 2004) provide testing functionality but it is focused on comparing the quality of the results, and not evaluating the “correctness” of the”
OpenAlea: a visual programming and component-based software platform for plant modelling. (2008, unknown): “In particular, the use of visual programming was introduced in different projects: AVS in scientific visualization (Upson et al. 1999), Vision (Sanner 2002) in bioinformatics or Orange (Demsar et al ., 2004) in data-mining.”
A user's guide to support vector machines. (2010, background): “The user has a large number of choices, and the following is an incomplete list of environments that provide an SVM classifier: Orange [20], The Spider (http://www.”