machine learningdecision treeRACT algorithminformation gainremote sensingC4.5 tree-induction algorithm is well known in machine learning and data mining fields, but it has slow speed with large tree size, especially in domains which have a preponderance of continuous attributes. In this paper, we ...
11. Bhukya DP, Ramachandram S. Decision tree induction-an approach for data classification using AVL–Tree.Int J Comp d Electrical Engineering.2010;2(4): 660–665. doi: 10.7763/IJCEE.2010.V2.208. [CrossRef] [Google Scholar] 12. Lin N, Noe D, He X. Tree-based methods and their appl...
This simplified algorithm is is the basis for all current top-down decision tree induction algorithm. Nevertheless, its assumptions are too stringent for practical use. For instance, it would only work if every combination of attribute values is present in the training data, and if the training ...
The basic principle, the advantageous properties of decision tree induction methods, and a description of the representation of decision trees so that a user can understand and describe the tree in a common way is given first. The overall decision tree induction algorithm is explained as well as...
The first machine learning scheme that we will develop in detail, the C4.5 algorithm, derives from the simple divide-and-conquer algorithm for producing decision trees that was described in Section 4.3. It needs to be extended in several ways before it is ready for use on real-world problems...
Other related work on the use of neural learning for decision tree induction consists of the works due to Sankar and Mammone, (14~ and Cios and Liu. (23) Sankar and Mammone use a heuristic algorithm, which mini- mizes the number of misclassifications at the output of a neuron, to ...
This investigation has been started in [38]. In [38], decision-making procedure of patients’ treatment has been considered based on development of decision tree (the algorithm C4.5 has been used for the classifier induction). 3.1. Fuzzy Classifier Induction The use of the fuzzy classifier ...
A Fast Decision Tree Learning Algorithm (AAAI 2006) Jiang Su, Harry Zhang [Paper] Anytime Induction of Decision Trees: An Iterative Improvement Approach (AAAI 2006) Saher Esmeir, Shaul Markovitch [Paper] When a Decision Tree Learner Has Plenty of Time (AAAI 2006) Saher Esmeir, Shaul Markov...
This paper addresses the problem of using decision lists for building machine learning algorithms. In this work, we first highlight the expressive power of Decision Lists (DL), which were already known to generalize decision trees. We also present ICDL, a new algorithm for learning simple decisio...
algorithm is empirically compared with Quinlan’s C4.5 (a common Decision Tree induction algorithm) using standard benchmark datasets. For most of the datasets used in the evaluation, the new algorithm is shown to extract De- cision Trees that have a higher predictive accuracy than those induced...