|
|
|
Foundation ::
Artificial Intelligence and Expert Systems ::
IND
|
IND
The IND Decision Tree Package
|
 |
Moderators:
Adopt This Application!
|
SOURCE CODE AVAILABLE
|
|
A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which has good prediction of classes on new data. Standard algorithms were developed by Breiman Friedman, Olshen and Stone; and Quinlan (ID3 and its successor C4.) As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis.
IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or it may be omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection.
IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in decision tree mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like the early version of C4. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves. IND also comes with a comprehensive experimental control suite.
IND consists of four basic kinds of routines:
- data manipulation routines,
- tree generation routines,
- tree testing routines,
- and tree display
routines.
The data manipulation routines are used to partition a single
large data set into smaller training and test sets. The generation routines
are used to build classifiers. The test routines are used to evaluate classifiers
and to classify data using a classifier. And the display routines
are used to display classifiers in various formats.
IND carries the NASA case number ARC-13188. It was originally released as part of the NASA COSMIC collection.
|
|
More software from National Technology Transfer Center
|
|
|
|
|
|
