By Ludmila I. Kuncheva
A unified, coherent remedy of present classifier ensemble tools, from basics of trend reputation to ensemble function choice, now in its moment edition
The paintings and technology of mixing trend classifiers has flourished right into a prolific self-discipline because the first version of Combining development Classifiers used to be released in 2004. Dr. Kuncheva has plucked from the wealthy panorama of modern classifier ensemble literature the subjects, tools, and algorithms that would consultant the reader towards a deeper figuring out of the basics, layout, and purposes of classifier ensemble methods.
Thoroughly up-to-date, with MATLAB® code and perform facts units all through, Combining development Classifiers includes:
- Coverage of Bayes determination idea and experimental comparability of classifiers
- Essential ensemble tools similar to Bagging, Random woodland, AdaBoost, Random subspace, Rotation woodland, Random oracle, and blunder Correcting Output Code, between others
- Chapters on classifier choice, range, and ensemble characteristic selection
With enterprise grounding within the basics of trend reputation, and that includes greater than a hundred and forty illustrations, Combining development Classifiers, moment Edition is a necessary reference for postgraduate scholars, researchers, and practitioners in computing and engineering.
Read Online or Download Combining Pattern Classifiers: Methods and Algorithms PDF
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Additional info for Combining Pattern Classifiers: Methods and Algorithms
In an M × K-fold cross validation, the data is split M times into K folds, and a cross-validation is performed on each such split. This procedure results in M × K estimates of P̂ D , whose average produces the desired estimate. A 10 × 10-fold cross-validation is a typical choice of such a protocol. r Leave-one-out. This is the cross-validation protocol where K = N, that is, one object is left aside, the classifier is trained on the remaining N − 1 objects, and the left out object is classified.
We are back to using the good old hold-out method, first because the others might be too time-consuming, and second, because the amount of data might be so excessive that small parts of it will suffice for training and testing. For example, consider a data set obtained from retail analysis, which involves hundreds of thousands of transactions. Using an estimate of the error over, say, 10,000 data points, can conveniently shrink the confidence interval and make the estimate sufficiently reliable.
For values N > 25, the following statistic is approximately normally distributed : z= √ 6 Function signrank this test. T − 14 N(N + 1) 1 N(N 24 . + 1)(2N + 1) from the Statistics Toolbox of MATLAB can be used to calculate the p-value for EXPERIMENTAL COMPARISON OF CLASSIFIERS 27 The sign test. A simpler but less powerful alternative to this test is the sign test. This time we do not take into account the magnitude of the differences, only their sign. By doing so, we further avoid the problem of noncommensurable errors or differences thereof.