In this paper, a new method of fuzzy decision trees called soft decision trees (SDT) is presented. This method combines tree growing and pruning, to determine the structure of the soft decision tree, with re4tting and back4tting, to improve its generalization capabilities. The method is explained and motivated and its behavior is 4rst analyzed empirically on 3 large databases in terms of classi...

This paper presents empirical evidence for five hypotheses about learning from large noisy domains: that trees built from very large training sets are larger and more accurate than trees built from even large subsets; that this increased accuracy is only in part due to the extra size of the trees; and that the extra training instances allow both better choices of attribute while building the tr...

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Quantum decision systems are being increasingly considered for use in artificial intelligence applications. Classical and quantum nodes can be distinguished based on certain correlations in their states. This paper investigates some properties of the states obtained in a decision tree structure. How these correlations may be mapped to the decision tree is considered. Classical tree representati...

While decision tree compilation is a promising way to carry out guard tests eeciently, the methods given in the literature do not take into account either the execution characteristics of the program or the machine-level tradeoos between diierent ways to implement branches. These methods therefore ooer little or no guidance for the implementor with regard to how decision trees are to be realize...

We propose an Extreme Point Tabu Search (EPTS) algorithm that constructs globally optimal decision trees for classiication problems. Typically, decision tree algorithms are greedy. They optimize the misclassiication error of each decision sequentially. Our non-greedy approach minimizes the misclassiication error of all the decisions in the tree concurrently. Using Global Tree Optimization (GTO)...

The search space for the feature selection problem in decision tree learning is the lattice of subsets of the available features. We provide an exact enumeration procedure of the subsets that lead to all and only the distinct decision trees. The procedure can be adopted to prune the search space of complete and heuristics search methods in wrapper models for feature selection. Based on this, we...

Decision trees have been already successfully used in medicine, but as in traditional statistics, some hard real world problems can not be solved successfully using the traditional way of induction. In our experiments we tested various methods for building univariate decision trees in order to find the best induction strategy. On a hard real world problem of the Orthopaedic fracture data with 2...

Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Selection of relevant features for spitting the tree nodes is a key property of their architecture, at the same time being their major shortcoming: the recursive nodes partitioning leads to geometric reduction of data quantity in the leaf nodes, which cause...