Best Classification of Continuous Data Based on Hybrid Decision Tree



Classification, Continuous Data, DBSCAN, Hybrid Decision Tree.


The correct classification of continuous data and finding classification algorithms with high accuracy to classify previously invisible records is one of the most important real problems facing researchers in the computing world, especially with continuous data, which always suffers from classification errors due to the slitting of the data into periods or discretization. In this paper, we present a hybrid algorithm that uses the principle of a decision tree, excluding the concepts of entropy and information gain in order to avoid splitting continuous data and replacing them to form the period based on concepts like the counters In the training process, the periods of continuous data will be self-dividing, meaning that the periods of continuous data are self-forming, which takes into account the composition of the period or split data based on the first and last element of the larger counter. And so on, right up to the counter with the fewest number of data points. The experimental result gave a very good rating of 95.56%, which means better results in the handling and classification of continuous data.


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a: Original Image, b: Segmented Image.




How to Cite

N. . Fadel, I. . K. Abbood, and H. . Qasem Gheni, “Best Classification of Continuous Data Based on Hybrid Decision Tree”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 388–392, Oct. 2022.