Induction of Decision Trees

Utgoff, P.E., 1989. Incremental Induction of Decision Trees. Machine Learning, 4, 161. 

The algorithm that we employ to build a classification decision tree from (x, y) data records belongs to a group of techniques known as top-down induction of decision trees (TDIDT) (Sonquist et al., 1971 Fu, 1968 Hunt, 1962 Quinlan, 1986, 1987, 1993 Breiman et al., 1984). 

Quinlan.., Induction of decision trees. Mach. Lean. 1, 81 (1986). 

Buontempo, F.V., Wang, X.Z., Mwense, M., Horan, N., Young, A., and Osborn, D. (2005) Genetic programming for the induction of decision trees to model ecotoxicity data. J. Chem. Inf. Model., 45, 904-912. 

For solving the pattern recognition problem encountered in the operation of chemical processes, the analysis of measured process data and extraction of process trends at multiple scales constitutes the feature extraction, whereas induction via decision trees is used for inductive... 

Induction via decision trees is a greedy procedure and does not guarantee optimality of the mapping, but works well in practice, as illustrated by successful applications in several areas. The attractive features of learning by decision trees are listed below. 

The first tree induction algorithm is called ID3 (Iterative Dichotomizer version 3) and was developed by Quinlan [38]. Subsequent improved versions of ID3 are C4.5 and C5. In our study, we used MC4 decision tree algorithm which is available in the MLC++ package [39]. MC4 and C4.5 use the same algorithm with different default parameter settings. 

Once the several records of a process variable have been generalized into a pattern, as indicated in the previous paragraph, we need a mechanism to induce relationships among features of the generalized descriptions. In this section we will discuss the virtues of inductive learning through decision trees. 

Inductive learning by decision trees is a popular machine learning technique, particularly for solving classification problems, and was developed by Quinlan (1986). A decision tree depicting the input/output mapping learned from the data in Table I is shown in Fig. 22. The input information consists of pressure, temperature, and color measurements of... 

Once the distinguishing features for each input variable have been extracted through the generalization of descriptions of the available records, these features become the inputs of the inductive learning procedure through decision trees. 

Inductive logic programming (ILP) is not a pharmacophore generation method by itself, but a subfield of the machine learning approach. In this field, other methods such as hidden Markov models, Bayesian learning, decision trees and logic programs are available. 

Method-like rule induction shall be available to convert tables or lists of examples into decision trees. 

B. K. Alsberg, R. Goodacre, J.J. Rowland and D.B. Kell, Classification of Pyrolysis Mass Spectra by Fuzzy Multivariate Rule Induction-comparison with Regression, K-nearest Neighbour, Neural and Decision-tree Methods. Analytica Chimica Acta, 348(1-3) (1997), 389 07. 

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