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Many classification algorithms provide evidence which is difficult to interpret.
There appear many classification methods in the literature.
In many classification cases, the labeled samples are difficult to acquire.
Thus far, many classification systems, including RMR, Q, and GSI, have been proposed in the literature.
Many classification methods have been proposed in the literature to tackle this problem.
There are many classification problems in petroleum reservoir characterisation, an example being the recognition of lithofacies from well log data.
Moreover, many classification and clustering algorithms are quite expensive in computational complexity.
Many classification techniques are proposed in literature, both supervised and unsupervised.
Past research studies have proposed many classification schemes for categorization of coal seams with respect to their spontaneous heating susceptibility.
Many classification algorithms require discrete values as the input and studies have shown that supervised discretization may improve classification performance.
Many classification problems must deal with imbalanced datasets where one class – the majority class – outnumbers the other classes.
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