Rule induction

Roles for an expert system can be learned by rule induction from a set of examples. This makes this method similar to the use of classifiers - it will solve problems of similar complexity and have similar disadvantages (e.g. possibility of unnoticed misclassifications). 

T A and H Kalayeh 1991. Applications of Neural Networks in Quantitative Structure-Activity ationships of Dihydrofolate Reductase Inhibitors, journal of Medicinal Chemistry 34 2824-2836. ik M and R C Glen 1992. Applications of Rule-induction in the Derivation of Quantitative icture-Activity Relationships. Journal of Computer-Aided Molecular Design 6 349-383. 

The easiest way to describe the rule induction capabilities of Rule-Master is to demonstrate its use with a relevant set of data. This data consists of information on a series of nonhalogenated aromatic compounds which were tested with five different glove materials. An arbitrary protective rating was assigned to each test based on the following breakthrough times ... 

Maximum acceptor superdelocalizability Artificial Neural Network Atomic Orbital Androgen Receptor Automated Rule-Induction... 

II. TYPES OF EXPERT SYSTEMS A. Automated Rule-Induction (ARI) Systems 1. The Nature of ARI Systems... 

TOPKAT Automated Rule-Induction Systems Accelrys Ltd. www.accelrys.com/products/topkat/ mutagenicity, teratogenicity, membrane irritation, neurotoxicity, and other effects Carcinogenicity,... 

Work on expert systems in the specific domain of tablet film coating was initiated in April 1990, using a rule induction tool in order to develop a system for the identification and solution of defects in film-coated tablets. Although not strictly a formulation expert system, the developed system did contain knowledge whereby a given formulation known to cause defects could be modified to provide a solution. The completed system described by Rowe and Upjohn. 

A-Razzak, M., Glen, R. C. Rule induction applied to the derivation of quantitative structure-activity relationships. In Methods and Principles in Medicinal Chemistry, Vol. 3. Advanced Computer-Assisted Techniques in Drug Discovery (van de Waterbeemd, H., Ed.), VCH Weinheim, 1995, pp. 319-331. 

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

B.K. Alsberg, M.K. Winson and D.B. Kell, Improving the Interpretation of Multivariate and Rule Induction Models by Using a Peak Parameter Representation, Chemometrics and Intelligent Laboratory Systems. 36(2) (1997), 95-109. 

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. 

B.K. Alsberg. W.G. Wade and R. Goodacre. Chemometric Analysis of Diffuse reflectance-absorbance Fourier Transform Infrared Spectra Using Rule Induction Methods Application to the Classification of Eubacterium Species, Applied Spectroscopy, 52(6) (1998), 823-832. 

The term decision trees is used here to cover a variety of related methods including rule induction and recursive partitioning. Decision trees classify compounds into local... 

Simple rule induction methods can also perform well compared to more sophisticated examples, particularly if the descriptors or attributes are well chosen. A rule is simple to express and interpretable, for example ... 

These simple rule induction and probability methods are straightforward to use and understand, and can often outperform more sophisticated approaches. They are sensitive to being skewed by redundant variables however, as well as by dependencies between attributes and the non-normal distributions of attribute values. These problems can be overcome by the careful selection of a sub-set of attributes and the use of other estimation methods for a more appropriate distribution for each attribute, although this is probably not necessary for most analyses. 

---