STreeD is a framework for optimal binary decision trees with separable optimization tasks. A separable optimization task is a task that can be optimized separately for the left and right subtree. The ...

Abstract: This research article identifies the fault occurrence in the blowfish cryptography algorithm using a modified Decision Tree classifier. Though there are several cryptography algorithms, the ...

Many scientific problems entail labeling data items with one of a given, finite set of classes based on features of the data items. For example, oncologists classify tumors as different known cancer ...

In total, 6 decision tree models were implemented, namely the classification and regression tree (CART), C5.0, GB, XGBoost, AdaBoost algorithm and random forest models. The Shapley additive ...

School of Information, Central University of Finance and Economics, Beijing, China. Personal credit risk is a part that both government and enterprises attach great importance to. A good personal ...

Decision trees are a simple but powerful prediction method. Figure 1: A classification decision tree is built by partitioning the predictor variable to reduce class mixing at each split. We can always ...

Abstract: Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The ...