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Proactive Data Mining Using Decision Trees

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Proactive Data Mining with Decision Trees

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

Abstract

In the previous chapter we introduced the task of proactive data mining and sketched an algorithmic framework for solving the task: first build a prediction model and then use it for optimization. In this chapter, we focus on decision tree classifiers and describe in detail two possible ways of implementing proactive data mining using: (a) a ready-made decision tree algorithm, and (b) a novel decision tree algorithm. We designed this latter algorithm to support the optimization phase of the proposed framework.

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References

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Authors and Affiliations

  1. Dept. of Industrial Engineering, Tel Aviv University, Ramat Aviv, Israel

    Haim Dahan & Oded Maimon

  2. Dept. of Industrial Engineering & Management, Shenkar College of Engineering and Design, Ramat Gan, Israel

    Shahar Cohen

  3. Information Systems Engineering, Ben-Gurion University, Beer-Sheva, Israel

    Lior Rokach

Authors
  1. Haim Dahan
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  2. Shahar Cohen
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  3. Lior Rokach
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  4. Oded Maimon
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Corresponding author

Correspondence to Lior Rokach .

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© 2014 The Author(s)

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Dahan, H., Cohen, S., Rokach, L., Maimon, O. (2014). Proactive Data Mining Using Decision Trees. In: Proactive Data Mining with Decision Trees. SpringerBriefs in Electrical and Computer Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0539-3_3

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  • DOI: https://doi.org/10.1007/978-1-4939-0539-3_3

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-0538-6

  • Online ISBN: 978-1-4939-0539-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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