Artificial neural networks with better analysis reliability in data mining

Abstract

If there are relatively few cases, semi-supervised learning approaches make advantage of a large amount of unlabeled data to assist develop a better classifier. To expand the labeled training set and update the classifier, a fundamental method is to select and label the unlabeled instances for which the current classifier has higher classification confidence. This approach is primarily used in two distinct semi-supervised learning paradigms: co-training and self-training. However, compared to self-labeled examples that would be tagged by a classifier, the real labeled instances will be more trustworthy. Incorrect label assignment to unlabeled occurrences might potentially compromise the classifier's accuracy in classification. This research presents a novel instance selection method based on actual labeled data. This will take into account the classifier's current performance on unlabeled data in addition to its performance on actual labeled data alone. This uses the accuracy changes in the newly trained classifier over the original labeled data as a criterion in each iteration to determine whether or not the selected most confident unlabeled examples would be accepted by a subsequent iteration. Naïve Bayes (NB) will be used as the basic classifier in the co-training and self-training studies. The findings indicate that the accuracy and categorization of self-training and co-training will be greatly enhanced by SIS. As compared to semi-supervised classification methods, it will enhance accuracy, precision, recall, and F1 score, according to the findings.