Classifying Sonar Signals Using an Incremental Data Stream Mining Methodology with Conflict Analysis

  • Simon FongEmail author
  • Suash Deb
  • Sabu Thampi
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 264)


Sonar signals recognition is an important task in detecting the presence of some significant objects under the sea. In military sonar signals are used in lieu of visuals to navigate underwater and/or locating enemy submarines in proximity. Specifically, classification in data mining is useful in sonar signal recognition in distinguishing the type of surface from which the sonar waves are bounced. Classification algorithms in traditional data mining approach offer fair accuracy by training a classification model with the full dataset, in batches. It is well known that sonar signals are continuous and they are collected in streaming manner. Although the earlier classification algorithms are effective for traditional batch training, it may not be practical for incremental classifier learning. Because sonar signal data streams can amount to infinity, the data pre-processing time must be kept to a minimum to fulfill the need for high speed. This paper introduces an alternative data mining strategy suitable for the progressive purging of noisy data via fast conflict analysis from the training dataset without the need to learn from the whole dataset at one time. Simulation experiments are conducted and superior results are observed in supporting the efficacy of the methodology.


Support Vector Machine Data Stream Classification Model Training Dataset Outlier Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Peyvandi, H., Farrokhrooz, M., Roufarshbaf, H., Park, S.-J.: SONAR Systems and Underwater Signal Processing: Classic and Modern Approaches. In: Kolev, N.Z. (ed.) Sonar Systems, pp. 173–206. InTech (2011)Google Scholar
  2. 2.
    China reveals its ability to nuke the US: Government boasts about new submarine fleet capable of launching warheads at cities across the nation. In: Daily Mail Online (2013), Available via Mail Online, (cited November 11, 2013)
  3. 3.
    Akbarally, H., Kleeman, L.: A Sonar Sensor for Accurate 3D Target Localization and Classification. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3003–3008 (1995)Google Scholar
  4. 4.
    Heale, A., Kleeman, L.: Fast target classification using sonar. In: Proceedings of 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 1446–1451 (2001)Google Scholar
  5. 5.
    Balleri, A.: Biologically inspired radar and sonar target classification. Doctoral thesis, University College London (2010)Google Scholar
  6. 6.
    Fong, S., Yang, H., Mohammed, S., Fiaidhi, J.: Stream-based Biomedical Classification Algorithms for Analyzing Biosignals. Journal of Information Processing Systems, Korea Information Processing Society 7(4), 717–732 (2011)CrossRefGoogle Scholar
  7. 7.
    Zhu, X., Wu, X.: Class Noise vs. Attribute Noise: A Quantitative Study. Artificial Intelligence Review 22(3), 177–210 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Hodge, V., Austin, J.: A Survey of Outlier Detection Methodologies. Artificial Intelligence Review 22(2), 85–126 (2004)CrossRefzbMATHGoogle Scholar
  9. 9.
    Aggarwal, C., Yu, P.: Outlier Detection for High Dimensional Data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2001)Google Scholar
  10. 10.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: Procceedings of the Conference of Management of Data (ACM SIGMOD 1996), pp. 103–114 (1996)Google Scholar
  11. 11.
    Arning, A., Agrawal, R., Raghavan, P.: A Linear Method for Deviation Detection in Large Databases. In: Proceedings of 1996 Int. Conf. Data Mining and Knowledge Discovery (KDD 1996), Portland, OR, pp. 164–169 (1996)Google Scholar
  12. 12.
    Xiong, H., Pandey, G., Steinbach, M., Kumar, V.: Enhancing Data Analysis with Noise Removal. IEEE Transactions on Knowledge and Data Engineering (TKDE) 18(3), 304–319 (2006)CrossRefGoogle Scholar
  13. 13.
    Brighton, H., Mellish, C.: Advances in Instance Selection for Instance-Based Learning Algorithms. Data Mining and Knowledge Discovery 6(2), 153–172 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Brodley, C.E., Friedl, M.A.: Identifying and Eliminating Mislabeled Training Instances. In: Proceedings of the 30th National Conference on Artificial Intelligence, pp. 799–805. AAI Press, Portland (1996)Google Scholar
  15. 15.
    John, G.H.: Robust Decision Tree: Removing Outliers from Databases. In: Proceedings of the First Int’l Conf. Knowledge Discovery and Data Mining, pp. 174–179 (1995)Google Scholar
  16. 16.
    Byeon, B., Rasheed, K., Doshi, P.: Enhancing the Quality of Noisy Training Data Using a Genetic Algorithm and Prototype Selection. In: Proceedings of the 2008 International Conference on Artificial Intelligence, Las Vegas, Nevada, USA, July 14-17, pp. 821–827 (2008)Google Scholar
  17. 17.
    Nanopoulos, A., Papadopoulos, A.N., Manolopoulos, Y., Welzer-Druzovec, T.: Robust Classification Based on Correlations Between Attributes. International Journal of Data Warehousing and Mining (IJDWM) 3(3), 1–17 (2007) ISSN: 1548-3924Google Scholar
  18. 18.
    Gorman, R.P., Sejnowski, T.J.: Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets. Neural Networks 1, 75–89 (1988)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of MacauTaipaMacau SAR
  2. 2.Cambridge Institute of TechnologyRanchiIndia
  3. 3.Indian Institute of Information Technology & ManagementKazhakkoottamIndia

Personalised recommendations