WEKA

WEKA: Waikato Environment for Knowledge Analysis. WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software’s functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project.


References in zbMATH (referenced in 204 articles , 1 standard article )

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  1. Abdulrahman, Salisu Mamman; Brazdil, Pavel; van Rijn, Jan N.; Vanschoren, Joaquin: Speeding up algorithm selection using average ranking and active testing by introducing runtime (2018)
  2. Amin, Talha; Moshkov, Mikhail: Totally optimal decision rules (2018)
  3. Cerutti, Federico; Vallati, Mauro; Giacomin, Massimiliano: On the impact of configuration on abstract argumentation automated reasoning (2018)
  4. Czajka, Łukasz; Kaliszyk, Cezary: Hammer for Coq: automation for dependent type theory (2018)
  5. de Caigny, Arno; Coussement, Kristof; de Bock, Koen W.: A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)
  6. Du, Wen Sheng; Hu, Bao Qing: A fast heuristic attribute reduction approach to ordered decision systems (2018)
  7. Feng, Zhen-Xing; Li, Qian-Zhong; Meng, Jian-Jun: Recognition of the long range enhancer-promoter interactions by further adding DNA structure properties and transcription factor binding motifs in human cell lines (2018)
  8. Malone, Brandon; Kangas, Kustaa; Järvisalo, Matti; Koivisto, Mikko; Myllymäki, Petri: Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction (2018)
  9. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  10. Olier, Ivan; Sadawi, Noureddin; Bickerton, G. Richard; Vanschoren, Joaquin; Grosan, Crina; Soldatova, Larisa; King, Ross D.: Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2018)
  11. Tunga, Burcu: A hybrid algorithm with cluster analysis in modelling high dimensional data (2018)
  12. van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin: The online performance estimation framework: heterogeneous ensemble learning for data streams (2018)
  13. Wistuba, Martin; Schilling, Nicolas; Schmidt-Thieme, Lars: Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2018)
  14. Ansótegui, Carlos; Bonet, Maria Luisa; Giráldez-Cru, Jesús; Levy, Jordi: Structure features for SAT instances classification (2017)
  15. Bagarello, Fabio; Cinà, Marco; Gargano, Francesco: Projector operators in clustering (2017)
  16. Barrett, Samuel; Rosenfeld, Avi; Kraus, Sarit; Stone, Peter: Making friends on the fly: cooperating with new teammates (2017)
  17. Chlebowski, Szymon; Komosinski, Maciej; Kups, Adam: Automated generation of erotetic search scenarios: classification, optimization, and knowledge extraction (2017)
  18. Chou, Chun-An; Bonates, Tibérius O.; Lee, Chungmok; Chaovalitwongse, Wanpracha Art: Multi-pattern generation framework for logical analysis of data (2017)
  19. Dehzangi, Abdollah; López, Yosvany; Lal, Sunil Pranit; Taherzadeh, Ghazaleh; Michaelson, Jacob; Sattar, Abdul; Tsunoda, Tatsuhiko; Sharma, Alok: PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction (2017)
  20. Escuín, David; Polo, Lorena; Ciprés, David: On the comparison of inventory replenishment policies with time-varying stochastic demand for the paper industry (2017)

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Further publications can be found at: http://www.cs.waikato.ac.nz/ml/publications.html