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Table 3 The results with Problem Context 1: classification error rates of the original data matrix and nine different data transformation techniques, with the corresponding standard deviation given in parenthesis These are obtained using 10-fold cross validation. The two lowest error rates on each investigated classifier are highlighted in boldface

Table 3 The results with Problem Context 1: classification error rates of the original data matrix and nine different data transformation techniques, with the corresponding standard deviation given in parenthesis These are obtained using 10-fold cross validation. The two lowest error rates on each investigated classifier are highlighted in boldface

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Fig. 1 Examples of a cluster ensemble, b label-assignment matrix, ¢ pairwise similarity matrix and d binary cluster-association (BA) matrix Note that X = {x1,...,x6}, = {1, 2, m3, 24}, m = {CI,CI, Ch}, mm = (CT, C3}, m3 = {CF}, C3} and ma = {CH CH, CH}
Fig. 1 Examples of a cluster ensemble, b label-assignment matrix, ¢ pairwise similarity matrix and d binary cluster-association (BA) matrix Note that X = {x1,...,x6}, = {1, 2, m3, 24}, m = {CI,CI, Ch}, mm = (CT, C3}, m3 = {CF}, C3} and ma = {CH CH, CH}
Table 2 Statistical details of numerical features
Table 2 Statistical details of numerical features
Table 4 The results with Problem Context 2: classification error rates of the original data matrix and nine different data transformatior techniques, with the corresponding standard deviation given in parenthesis
Table 4 The results with Problem Context 2: classification error rates of the original data matrix and nine different data transformatior techniques, with the corresponding standard deviation given in parenthesis
These are obtained using 10-fold cross validation. The two lowest error rates on each investigated classifier are highlighted in boldface
These are obtained using 10-fold cross validation. The two lowest error rates on each investigated classifier are highlighted in boldface
Fig. 4 Error rates of different transformation techniques for the first problem context, with respect to each class
Fig. 4 Error rates of different transformation techniques for the first problem context, with respect to each class
Fig. 5 Error rates of different transformation techniques for the second problem context, with respect to each class
Fig. 5 Error rates of different transformation techniques for the second problem context, with respect to each class
of data for classifier ensembles has been introduced by Verma and Rahman [55]. This has inspired the on-going work, which aims to examine the application of WCT-T and WTQ-T in such a context. References
of data for classifier ensembles has been introduced by Verma and Rahman [55]. This has inspired the on-going work, which aims to examine the application of WCT-T and WTQ-T in such a context. References
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