Lavi Yaron, Abdallah Loai


In this study, we propose an improvement on a commonly used classification algorithm – Decision Tree. The mentioned improvement has been observed during segmentation of colored images. The method in which we improved the performance of a regular decision tree, involved clustering the data which was then used as the training set for the decision tree. The clustering algorithm used was K-Means. After running both regular and improved decision tree algorithms several times, on different images of animals in a diverse set of backgrounds, we concluded that the decision tree algorithm that was empowered by the clustering of K-Means, had higher confidence levels and performance.


decision trees, k-means, computer vision, ensemble clustering

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