Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

Cantu-Paz, Erick; Kamath, Chandrika

Title: Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

Full Record
Other Related Research

Abstract

A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

Inventors:

Cantu-Paz, Erick ^[1]; Kamath, Chandrika ^[2]

Oakland, CA
Tracy, CA

Issue Date:: 2006-06-13

Research Org.:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 908394

Patent Number(s):: 7062504

Application Number:: 10/133,992

Assignee:: The Regents of the University of California (Oakland, CA)

Patent Classifications (CPCs):: G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y10 - TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S - TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS

Show more

G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G06N3/126 - {Genetic algorithms, i.e. information processing using digital simulations of the genetic system}

Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y10 - TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S - TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
Y10S707/968 - Partitioning
Y10S707/99943 - Generating database or data structure, e.g. via user interface

G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA
G06K9/6282 - {Tree-organised sequential classifiers}

Show less

DOE Contract Number:: W-7405-ENG-48

Resource Type:: Patent

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Cantu-Paz, Erick, and Kamath, Chandrika. Creating ensembles of oblique decision trees with evolutionary algorithms and sampling.  United States: N. p., 2006. 
        Web.

Copy to clipboard


                    Cantu-Paz, Erick, & Kamath, Chandrika. Creating ensembles of oblique decision trees with evolutionary algorithms and sampling.  United States.

Copy to clipboard


                    Cantu-Paz, Erick, and Kamath, Chandrika. Tue .  
        "Creating ensembles of oblique decision trees with evolutionary algorithms and sampling".  United States.  https://www.osti.gov/servlets/purl/908394.

Copy to clipboard


                    
@article{osti_908394,

  title        = {Creating ensembles of oblique decision trees with evolutionary algorithms and sampling},

  author       = {Cantu-Paz, Erick and Kamath, Chandrika},

  abstractNote = {A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2006},

  month        = {6}

}

Copy to clipboard

Patent:

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE Patents and OSTI.GOV collections:

Creating ensembles of decision trees through sampling

Patent Kamath, Chandrika; Cantu-Paz, Erick

A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decisionmore » trees in ensembles. « less
Full Text Available
Using histograms to introduce randomization in the generation of ensembles of decision trees

Patent Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around themore » « less
Full Text Available
Parallel object-oriented decision tree system

Patent Kamath, Chandrika; Cantu-Paz, Erick

A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.
Full Text Available
Combining evolutionary algorithms with oblique decision trees to detect bent double galaxies

Conference Cantu-Paz, E; Kamath, C

Decision trees have long been popular in classification as they use simple and easy-to-understand tests at each node. Most variants of decision trees test a single attribute at a node, leading to axis-parallel trees, where the test results in a hyperplane which is parallel to one of the dimensions in the attribute space. These trees can be rather large and inaccurate in cases where the concept to be learnt is best approximated by oblique hyperplanes. In such cases, it may be more appropriate to use an oblique decision tree, where the decision at each node is a linear combination ofmore » « less
DOI: https://doi.org/10.1117/12.403609

Full Text Available
Using Evolutionary Algorithms to Induce Oblique Decision Trees

Conference Cantu-Paz, E; Kamath, C

This paper illustrates the application of evolutionary algorithms (EAs) to the problem of oblique decision tree induction. The objectives are to demonstrate that EAs can find classifiers whose accuracy is competitive with other oblique tree construction methods, and that this can be accomplished in a shorter time. Experiments were performed with a (1+1) evolutionary strategy and a simple genetic algorithm on public domain and artificial data sets. The empirical results suggest that the EAs quickly find Competitive classifiers, and that EAs scale up better than traditional methods to the dimensionality of the domain and the number of training instances.
Full Text Available

Similar Records