Classification decision trees, inductive

Classification Decision Trees and Their Inductive Construction... 

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). 

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... 

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. 

Alsberg, B. K., Goodacre, R., Rowland, J. J. and Kell, D. B. (1997) Classification of pyrolysis mass spectra by fuzzy multivariate rule induction comparison with regression, K-nearest neighbour, neural and decision-tree methods. Analytica Chimica Acta, in press. 

During the retrieval, the attributes of the target case and the source cases are compared to find the most similar case. There are two widely used retrieval techniques (Watson, 1997) nearest neighbor and inductive retrieval. The nearest neighbor retrieval simply calculates the differences of the attributes, multiplied by a weighting factor. In inductive retrieval a decision tree is produced, which classifies or indexes the cases. There are classification questions about the main attributes in the nodes of the tree, by answering these questions the most similar case is found. 

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