Abstract
This paper studies the problem of mining diversified sets of shared decision trees (SDTs). Given two datasets representing two application domains, an SDT is a decision tree that can perform classification on both datasets and it captures class-based population-structure similarity between the two datasets. Previous studies considered mining just one SDT. The present paper considers mining a small diversified set of SDTs having two properties: (1) each SDT in the set has high quality with regard to “shared” accuracy and population-structure similarity and (2) different SDTs in the set are very different from each other. A diversified set of SDTs can serve as a concise representative of the huge space of possible cross-domain similarities, thus offering an effective way for users to examine/select informative SDTs from that huge space. The diversity of an SDT set is measured in terms of the difference of the attribute usage among the SDTs. The paper provides effective algorithms to mine diversified sets of SDTs. Experimental results show that the algorithms are effective and can find diversified sets of high quality SDTs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beer, D.G., et al.: Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nature Medicine 8, 816–824 (2002)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Christie, S., Gentner, D.: Where hypotheses come from: Learning new relations by structural alignment. Journal of Cognition and Development 11(3), 356–373 (2010)
Dong, G.: Cross domain similarity mining: Research issues and potential applications including supporting research by analogy. ACM SIGKDD Explorations (June 2012)
Dong, G., Han, Q.: Mining accurate shared decision trees from microarray gene expression data for different cancers. In: International Conference on Bioinformatics and Computational Biology, BIOCOMP 2013 (2013)
Fauconnier, G.: Mappings in Thought and Language. Cambridge University Press (1997)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: ICML, pp. 148–156 (1996)
Gentner, D.: Structure mapping: A theoretical framework for analogy. Cognitive Science 7, 155–170 (1983)
Gentner, D., Colhoun, J.: Analogical processes in human thinking and learning. In: Glatzeder, B., Goel, V., von Müller, A. (eds.) Towards a Theory of Thinking. On Thinking, vol. 2. Springer, Heidelberg (2010)
Gentner, D., Markman, A.B.: Structure mapping in analogy and similarity. American Psychologist 52(1), 45–56 (1997)
Gordon, G.J., et al.: Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research 62, 4963–4967 (2002)
Han, Q., Dong, G.: Using attribute behavior diversity to build accurate decision tree committees for microarray data. J. Bioinformatics and Computational Biology 10(4) (2012)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51(2), 181–207 (2003)
Li, J., Liu, H.: Ensembles of cascading trees. In: ICDM, pp. 585–588 (2003)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)
Pomeroy, S.L., et al.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436–442 (2002)
Shipp, M.A., et al.: Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine 8, 68–74 (2002)
Singh, D., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2), 203–209 (2002)
Tong, W., et al.: ArrayTrack-Supporting toxicogenomic research at the FDA’s National Center for Toxicological Research (NCTR). EHP Toxicogenomics 111(15), 1819–1826 (2003)
Van’t Veer, L.J., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Dong, G., Han, Q. (2014). Mining Diversified Shared Decision Tree Sets for Discovering Cross Domain Similarities. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-06605-9_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06604-2
Online ISBN: 978-3-319-06605-9
eBook Packages: Computer ScienceComputer Science (R0)