Oblique Multicategory Decision Trees Using Nonlinear Programming
نویسنده
چکیده
I of decision trees is a popular and effective method for solving classification problems in data-mining applications. This paper presents a new algorithm for multi-category decision tree induction based on nonlinear programming. This algorithm, termed OC-SEP (Oblique Category SEParation), combines the advantages of several other methods and shows improved generalization performance on a collection of real-world data sets.
منابع مشابه
A new way to build Oblique Decision Trees using Linear Programming
Adding linear combination splits to decision trees allows multivariate relations to be expressed more accurately and succinctly than univariate splits alone. In order to determine an oblique hyperplane which distinguishes two sets, linear programming is proposed to be used. This formulation yields a straightforward way to treat missing values. Computational comparison of that linear programming...
متن کاملA System for Induction of Oblique Decision
This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to nd a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or...
متن کاملA System for Induction of Oblique Decision Trees
This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to nd a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or...
متن کاملDiscovery of Relevant New Features by Generating Non-Linear Decision Trees
field of manufacturing new features. Most decision tree algorithms using selective induction focus on univariate, i.e. axis-parallel tests at each internal node of a tree. Oblique decision trees use multivariate linear tests at each non-leaf node. One well-known limitation of selective induction algorithms, however, is its inadequate description of hypotheses by task-supplied original features....
متن کاملHHCART: 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 کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- INFORMS Journal on Computing
دوره 17 شماره
صفحات -
تاریخ انتشار 2005