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IND: Creation and Manipulation of Decision Trees from Data

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 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 a Breiman/Friedman/ Olshen/Stone-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like C4.5. 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 consist 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 is written in K&R C, with controlling scripts in the "csh" shell of UNIX, and extensive UNIX man entries. It is designed to be used on any UNIX system, although it has only been thoroughly tested on SUN platforms. IND comes with a manual giving a guide to tree methods, and pointers to the literature, and several companion documents.


  • IND: Creation and Manipulation of Decision Trees from Data
    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 (hopefully) has good prediction of classes on new data. Standard algorithms are that of Breiman, Friedman, Olshen, and Stone; and 2) Id3 and its successor C4 (by Quinlan). 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.
This software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.1 or later.
IND: Creation and Manipulation of Decision Trees from Data NOSA Software Agreement
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