Decision Tree and Decision Forest Algorithms: On Improving Accuracy, Efficiency and Knowledge Discovery

Md Nasim Adnan

    Research output: ThesisDoctoral Thesis

    231 Downloads (Pure)


    The "Digital Revolution" has blessed the human civilization with enormous amount of "DATA". The challenges of automatically analyzing these data has augmented the need for developing sophisticated means for data mining. In the field of data mining, classification plays a very important role in both predicting the “class” of an unseen instance and discovering patterns in data. In today’s data-driven world, classification is being applied in our day-to-day activity. Thus, the importance of improving prediction accuracy and simplifying knowledge discovery from classifiers are paramount.
    Decision tree is one of the most popular classifiers that is capable of predicting unseen instances with high accuracy and generating human-interpretable knowledge. Besides, due to their sensitive nature decision trees are often used as base classifiers to form ensembles of decision trees. An ensemble of decision trees, popularly known as a decision forest is said to be more robust to noise(s) and more accurate than a single decision tree.
    Many decision tree and decision forest building algorithms have been proposed in literature. However, the existing algorithms have various limitations that give us room for further improvement. Furthermore, decision forests are more memory-intensive and less knowledge-extractable than a single decision tree. Hence, in this thesis we propose several novel algorithms for improving accuracy of decision trees and decision forests, then propose a technique to reduce the size of decision forests while retaining or increasing the ensemble accuracy, and finally propose a framework for effective knowledge discovery from decision forests. In order to validate the proposed algorithms/techniques, we carry out extensive experiments on several publicly available data sets. The experimental results indicate that the proposed algorithms/techniques clearly improve the current state-of-the-art in applicable areas.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • Charles Sturt University
    • Islam, Zahid, Principal Supervisor
    Award date31 Jul 2017
    Publication statusPublished - 2017


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