In this paper, we consider decision trees that use both conventional queries based on one attribute each and hypotheses about values of all attributes. Such are similar to ones studied in exact learning, where membership equivalence allowed. We present dynamic programming algorithms for minimization the depth above discuss results computer experiments various data sets randomly generated Boolea...

Credit scoring models help lenders decide whether to grant or reject credit applicants. This paper proposes a model based on boosted decision trees, powerful learning technique that aggregates several trees form classifier given by weighted majority vote of classifications predicted individual trees. The performance is evaluated using two publicly available card application datasets. prediction...

This paper presents a new decision tree learning algorithm, fuzzy min-max decision tree (FMMDT) based on fuzzy min-max neural networks. In contrast with traditional decision trees in which a single attribute is selected as the splitting test, the internal nodes of the proposed algorithm contain a fuzzy min-max neural network. In the proposed learning algorithm, the exibility inherent in the fuz...

In this study, we examined the effect of example cases with confounding values on decision trees constructed for six otoneurological diseases involving vertigo. The six diseases were benign positional vertigo, Menière’s disease, sudden deafness, traumatic vertigo, vestibular neuritis, and vestibular schwannoma. Patient cases with confounding values were inserted into original vertigo data and d...

In this paper, we assume that a dispersed data is represented by finite set S of decision tables with equal sets attributes. We discuss one the possible ways to study trees common all from S: building table for which coincides S. show when can build such and how it in polynomial time. If have table, apply various tree learning algorithms. extend considered approach rules test (reducts)

conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...