منابع مشابه
HHCART: An Oblique Decision Tree
Decision trees are a popular technique in statistical data classification. They recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. The basic Classification and Regression Tree (CART) algorithm partitions the feature space using axis parallel splits. When the true decision boundaries are not aligned with...
متن کاملAn Evolutionary Algorithm for Oblique Decision Tree Induction
In the paper, a new evolutionary approach to induction of oblique decision trees is described. In each non-terminal node, the specialized evolutionary algorithm is applied to search for a splitting hyperplane. The feature selection is embedded into the algorithm, which allows to eliminate redundant and noisy features at each node. The experimental evaluation of the proposed approach is presente...
متن کاملOblique Decision Tree Learning Approaches - A Critical Review
Decision tree classification techniques are currently gaining increasing impact especially in the light of the ongoing growth of data mining services. A central challenge for the decision tree classification is the identification of split rule and correct attributes. In this context, the article aims at presenting the current state of research on different techniques for classification using ob...
متن کاملAn Evolving Oblique Decision Tree Ensemble Architecture for Continuous Learning Applications
We present a system architecture for evolving classifier ensembles of oblique decision trees for continuous or online learning applications. In continuous learning, the classification system classifies new instances for which after a short while the true class label becomes known and the system then receives this feedback control to improve its future predictions. We propose oblique decision tr...
متن کاملReal Boosting a la Carte with an Application to Boosting Oblique Decision Tree
In the past ten years, boosting has become a major field of machine learning and classification. This paper brings contributions to its theory and algorithms. We first unify a well-known top-down decision tree induction algorithm due to [Kearns and Mansour, 1999], and discrete AdaBoost [Freund and Schapire, 1997], as two versions of a same higher-level boosting algorithm. It may be used as the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2016
ISSN: 0167-9473
DOI: 10.1016/j.csda.2015.11.006