A comparison of classification in artificial intelligence, induction versus a self-organising neural networks

Abstract Three methods of classification (machine learning) were used to produce a program to choose a detector for ion chromatography (IC). The selected classification systems were: C4.5, an induction method based on an information theory algorithm; INDUCT, which is based on a probability algorithm and a self-organising neural network developed specifically for this application. They differ both in the learning strategy employed to structure the knowledge, and the representation of knowledge acquired by the system, i.e., rules, decision trees and a neural network. A database of almost 4000 cases, that covered most IC experiments reported in the chemical literature in the period 1979 to 1989, comprised the basis for the development of the system. Generally, all three algorithms performed very well for this application. They managed to induce rules, or produce a network that had about a 70% success rate for the prediction of detectors reported in the publication and over 90% success for choosing a detector that could be used for the described method. This was considered acceptable due to the nature of the problem domain and that of the training set. Each method effectively handled the very high noise levels in the training set and was able to select the relevant attributes.