Search

The article titled "Retraction Note: A taxonomy on impact of label noise and feature noise using machine learning techniques" has been retracted by the publisher due to concerns about compromised editorial handling and peer-review processes. The investigation found issues with inappropriate references and the article not being in line with the journal's scope. The authors have not responded to correspondence regarding the retraction, and the publisher no longer has confidence in the results and conclusions of the article. [Extracted from the article]

Soft computing techniques are effective techniques that are used in prediction of noise in the dataset which causes misclassification. In classification, it is expected to have perfect labeling, but the noise present in data has impact on the label mapped and influences the input values by affecting the input feature values of the instances. Existence of noise complicates prediction in the real-world data which leads to vicious effect of the classifier. Present study aims at quantitative assessment of label noise and feature noise through machine learning, and classification performance in medical datasets as noise handling has become an important aspect in the research work related to data mining and its application. Weak classifier boosting provides high standard accuracy levels in classification problems. This study explores the performance of most recent soft computing technique in machine learning which includes weak learner-based boosting algorithms, such as adaptive boosting, generalized tree boosting and extreme gradient boosting. Current study was made to compare and analyze disparate boosting algorithms in divergent noise and feature levels (5%, 10%, 15% and 20%) on distinct medical datasets. The performances of weak learners are measured in terms of accuracy and equalized loss of accuracy. [ABSTRACT FROM AUTHOR]

With the rapid expansion of e-commerce over the decades, the growth of the user generated content in the form of reviews is enormous on the Web. A need to organize the e-commerce reviews arises to help users and organizations in making an informed decision about the products. Opinion mining systems based on machine learning approaches are used online to categorize the customer opinion into positive or negative reviews. Different from previous approaches that employed single rule based or statistical techniques, we propose a hybrid machine learning approach built under the framework of combination (ensemble) of classifiers with principal component analysis (PCA) as a feature reduction technique. This paper introduces two hybrid models, i.e. PCA with bagging and PCA with Bayesian boosting models for feature based opinion classification of product reviews. The results are compared with two individual classifier models based on statistical learning i.e. logistic regression (LR) and support vector machine (SVM). We found that hybrid methods do better in terms of four quality measures like misclassification rate, correctness, completeness and effectiveness in classifying the opinion into positive and negative. [ABSTRACT FROM AUTHOR]