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Many classification algorithms provide evidence which is difficult to interpret.
Many classification algorithms require discrete values as the input and studies have shown that supervised discretization may improve classification performance.
Many classification algorithms are known to perform poorly on such high dimensional data.
Since V3 is variable in length, many classification algorithms are not applicable.
Many classification algorithms have been proposed to solve different classification problems.
Among the many classification algorithms available, we chose five one-class algorithms to compare for miRNA discovery.
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With the new method, we got much better results in terms of both training and testing data accuracies than many other classification algorithms like minimum Euclidean distance, Fisher linear likelihood and extraction and classification of homogeneous objects, which is a spectral-spatial classifier algorithm.
There are many different classification algorithms, for instance, support vector machines, neural networks, naive Bayes, and the decision trees.
Moreover, many classification and clustering algorithms are quite expensive in computational complexity.
Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms.
A common solution, which would enable the exploitation of many widely-used classification algorithms, like SVMs or artificial neural networks, is to use the so-called "one-hot" encoding.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com