A zero-norm feature selection method for improving the performance of the one-class machine learning for microRNA target detection

The application of one-class machine learning is gaining attention in the computational biology community. Different studies have described the use of two-class machine learning to predict microRNAs (miRNAs) gene target. Most of these methods require the generation of an artificial negative class. However, designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and/or yield a biased estimate of performance. We present study using one-class machine learning for miRNA target discovery and compare one-class to two-class approaches using a zero-norm for feature selection. The usage of this simple feature selection cause an improving of the one-class results, where in some cases reaches the performance of the two-class approach. Of all the one-class methods tested based on the all features, we found that most of them gave similar accuracy that range from 0.81 to 0.89 while the two-class gave 0.93-0.99 accuracy. Interestingly, using zero-norm feature selection improves the results to reach accuracy of about 0.96. One and two class methods can both give useful classification accuracies. The advantage of one class methods is that they don't require any additional effort for choosing the best way of generating the negative class. In these cases one-class methods can be superior to two-class methods when the features which are chosen as representative of that positive class are well defined.