By K A E Totton, P G Flavin (auth.), P. G. Flavin, K. A. E. Totton (eds.)
In bringing jointly this publication, the editors have stored pursuits in brain. first of all, the aim of training the reader via giving an perception into the wealth of computing and mathematical options now getting used to construct choice aid structures. Secondly, of aiming to stimulate the mind's eye by way of together with an eclectic mixture of contributions from a variety of company components to illustrate that there's no box within which smooth choice aid innovations can't usefully be utilized. The quintessence of choice aid structures is they are designed to help humans in developing the easiest plan of action in a given scenario yet to not automate or inform them prescriptively easy methods to in attaining a goal.
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3 51 Analysing the statistical significance of classification tests A common starting point for the statistical analysis of classification results is a confusion matrix, originally defined by Massy . A confusion matrix summarizes the number of correct and incorrect classifications made by a classifier. Suppose there are two possible classifications from a set of data, A and B and the data comprises N examples. Then the confusion matrix will, in general, look like: Actual Classification Predicted Classification where: is the nl2 is the n21 is the nn is the nll number number number number A B A nll nl2 'B n21 n22 of of of of examples examples examples examples of of of of class class class class A, B, A, B, correctly classified, incorrectly classified, incorrectly classified, correctly classified, The standard statistical method for testing the significance of the results from a classifier is to use a chi-squared test [2, 42].
15. Quinlan J R: 'Learning efficient classification procedures and their application to chess end games' in Michalski R, Carbonnel J and Mitchell T (Eds): 'Machine learning: an artificial intelligence approach', Palo Alto! Tioga (1983). 16. Forsyth R and Rada R: 'Machine intelligence: Applications in expert systems and information retrieval', p 59-64 (1986). 17. Brieman L et al: 'Classification and regression trees', Wadsworth, Monterrey, CA (1984). 18. 0', Technical Report TI/P2154/RAB54/, Turing Institute (Jan 1990).
As before, data is split into a training and test set and the training and test processes carried out. The idea is extended by creating (n - 1) further training and test sets from the same data using random selection. The training and testing process is then repeated for this new selection. The overall results can be analysed to show trends and test for statistical significance. e. some of the original data examples will not be present and will be replaced by duplicates of other examples. The result is a new random sample of size n, taken from the original data, also of size n.