Research Publications for Geoffrey Holmes

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Publications ByHOLMES, Geoffrey

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  • Holmes, G., Liu, T. Y., Li, H., King, I., Sugiyama, M., & Zhou, Z. H. (2017). Introduction: Special Issue of Selected Papers from ACML 2015. Machine Learning, 106(4), 459-461. doi:10.1007/s10994-017-5636-6

  • Durrant, R. J., Kim, K. E., Holmes, G., Marsland, S., Sugiyama, M., & Zhou, Z. H. (2017). Foreword: Special issue for the Journal Track of the 8th Asian Conference on Machine Learning (ACML 2016). Machine Learning, 106(5), 623-625. doi:10.1007/s10994-017-5637-5

  • Bifet, A., Zhang, J., Fan, W., He, C., Zhang, J., Qian, J., . . . Pfahringer, B. (2017). Extremely fast decision tree mining for evolving data streams. In Proc 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1733-1742). Conference held Halifax, NX, Canada: ACM. doi:10.1145/3097983.3098139

  • Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfharinger, B., . . . Abdessalem, T. (2017). Adaptive random forests for evolving data stream classification. Machine Learning, (Online First), 27 pages. doi:10.1007/s10994-017-5642-8

  • Read, J., Reutemann, P., Pfahringer, B., & Holmes, G. (2016). MEKA: A multi-label/multi-target extension to WEKA. Journal of Machine Learning Research, 17(21), 1-5.

  • van Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2015). Having a Blast: meta-learning and heterogeneous ensembles for data streams. In Proc IEEE International Conference on Data Mining (pp. 1003-1008). Atlantic City, USA: IEEE. doi:10.1109/ICDM.2015.55

  • Bifet, A., de Francisci Morales, G., Read, J., Holmes, G., & Pfahringer, B. (2015). Efficient online evaluation of big data stream classifiers. In Proc 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 59-68). Sydney, Australia: ACM. doi:10.1145/2783258.2783372

  • Žliobaite, I., Bifet, A., Read, J., Pfahringer, B., & Holmes, G. (2015). Evaluation methods and decision theory for classification of streaming data with temporal dependence. Machine Learning, 98(3), 455-482. doi:10.1007/s10994-014-5441-4

  • van Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2015). Case study on bagging stable classifiers for data streams. In Twenty-fourth Belgian-Dutch Conference on Machine Learning. Delft, Netherlands.

  • Holmes, G., & Liu, T. -Y. (2015). (Editors) Proceedings of the 7th Asian Conference on Machine Learning, JMLR Workshop and Conference Proceedings 45. In ACML 2015 (pp. 438 pages). Hong Kong: JMLR. Retrieved from

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