Abstract
With the rapid development of information technology, the efficiency of information management has drawn increasing importance with its broadening application. Hence, a new information classification algorithm has proposed in this paper so as to improve information management of the limited resources by reducing its complexity. However, ID3 algorithm is a classical and imprecise algorithm in data mining, because traditional ID3 algorithm selects the attribute that has the maximum information gain according to the data set as that of the split node. Then the data subset is further divided according to the number of attribute values, and the information gain of each subset is calculated recursively. Decision Tree Optimization Ratio is the core approach in this algorithm, whose basic ideas have been introduced and analysed, proving to be more complex. Therefore, the authors propose a relatively precise RLBOR algorithm which takes the number of nodes in the decision tree model into consideration. The experiment show more precise of RLBOR algorithm.
Similar content being viewed by others
References
Dobra, A., Garofalakis, M., Gehrke, J. and Rastogi, R.: Processing complex aggregate queries over data streams. In: Proc. 2002 ACM Sigmod Int. Conf. Management of Data, Madison, WI, pp. 90–98 (2002)
Rajgopal Kannan, K., Sarangi, S., Ray, S. and Sitharama Iyengar, S.: Minimal sensor integrity in sensor grids. In: Proc. Int. Conf. Parallel Processing, pp. 21–27 (2002)
Quinlan, J.R.: Induction of decision trees, Machine Learning, pp. 257–264 (1986)
Liu, Y., Pi, D., Cheng, Q.: Ensemble kernel method: SVM classification based on game theory. J. Syst. Eng. Electr. 27, 251–259 (2016)
Antal, M., Bokor, Z., Szabó, L.Z.: Information revealed from scrolling interactions on mobile devices. Pattern Recognit. Lett. 56, 7–13 (2015)
Khatami, R., Mountrakis, G., Stehman, S.V.: A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research. Remote Sens. Environ. 177, 89–100 (2016)
Lee, J., kim, D.W.: Memetic feature selection algorithm for multi-label classification. Inf. Sci. 93, 80–96 (2015)
Kavzoglu, T., Colkesen, I., Yomralioglu, T.: Object-based classification with rotation forest ensemble learning algorithm using very-high-resolution WorldView-2 image. Remote Sens. Lett. 6, 834–843 (2015)
Jiang, W.X., Zhong, Y.Z., Liang, H.: An Evaluation model of polytechnic teaching quality based on ID3 decision making Tree. In: 3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS), Huhhot, China, pp. 2437–2440 (2014)
Li, J.F., Lei, J.H., Zhao, X.X.: An improved ID3 algorithm. In: 2nd International Conference on Advances in Computational Modeling and Simulation (ACMS 2013), Kunming, China, pp. 723–727 (2014)
Ludwig, S.A., Jakobovic, D., Picek, S.: Analyzing gene expression data: fuzzy decision tree algorithm applied to the classification of cancer data, Fuzzy Systems (FUZZ-IEEE). In: 2015 IEEE International Conference on. IEEE, pp. 1–8 (2015)
Chhipi-Shrestha, G., Mori, J., Hewage, K., et al.: Clostridium difficile infection incidence prediction in hospitals (CDIIPH): a predictive model based on decision tree and fuzzy techniques’. Stoch. Environ. Res. Risk Assess. 31, 417–430 (2017)
**, C.X., Li, F.C., Li, Y.: A generalized fuzzy ID3 algorithm using generalized information entropy, knowledge-based systems, pp. 13–21(2014)
Ahmadi, E., Javadi, H., Khansefid, A., et al.: Fuzzy decision tree learning for preoperative classification of adnexal masses. In: 4th International Conference on Health Informatics (HEALTHINF 2011), Rome, ITALY, pp. 364–375 (2011)
Zeinalkhani, M., Eftekhari, M.: Fuzzy partitioning of continuous attributes through discretization methods to construct fuzzy decision tree classifiers. Inf. SCi. 278(26), 715–735 (2014)
Gokgoz, E., Subasi, A.: Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed. Signal. Process. Control 18, 138–144 (2015)
Vlahovic, N.: An evaluation framework and a brief survey of decision tree tools. In: 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, CROATIA, pp. 1299–1304 (2016)
Hameed, A., Dai, R., Balas, B.: A decision-tree-based perceptual video quality prediction model and its application in FEC for wireless multimedia communications. IEEE Trans. Multimed 18, 764–774 (2016)
Ronowicz, J., Thommes, M., Kleinebudde, P., et al.: A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm. Eur. J. Parm. Sci. 73, 44–48 (2015)
Hashemi, S.A.H., Ghodrati Amiri, A.C., Hamedi, F.: Steel buildings damage classification by damage spectrum and decision tree algorithm. J. Rehabil. Civil Eng. 3, 24–42 (2015)
Hong, J.R., Ding, M.F., Li, X.Y., Wang, L.W.: A new algorithm of decision tree induction. Chin. J. Comput. 18, 470–474 (1995)
Wang, X.H., Wang, L.L., Li, N.F.: An application of decision tree based on ID3. Phys. Procedia 25, 1017–1021 (2012)
Acknowledgements
This work was funded by the National Natural Science Foundation of China under Grant (Nos. 61772152, 61502037), and the Basic Research Project (Nos. JCKY2016206B001, JCKY2014206C002 and JCKY2017604C010).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, H., Wang, T., Zhou, Y. et al. Information classification algorithm based on decision tree optimization. Cluster Comput 22 (Suppl 3), 7559–7568 (2019). https://doi.org/10.1007/s10586-018-1989-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-1989-2