The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection

Abstract

Purpose The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets. Design/methodology/approach The present study proposed a new effective model based on fuzzy C-means (FCM), ensemble filtering (ENS) and machine learning algorithms, called an FCM-ENS model. This model is mainly composed of three parts: noise detection, noise filtering and noise classification. Findings The performance of the proposed model was tested by conducting experiments on six data sets from the UCI repository. As shown by the obtained results, the proposed noise detection model very effectively detected the class noise and enhanced performance in case the identified class noisy instances were removed. Originality/value To the best of the authors’ knowledge, no effort has been made to improve the FCM algorithm in relation to class noise detection issues. Thus, the novelty of existing research is combining the FCM algorithm as a noise detection technique with ENS to reduce the negative effect of inherent noise and increase data quality and accuracy.

A hybrid model for class noise detection using k-means and classification filtering algorithms

“…The accuracy formula is applied to calculate the performance of the proposed technique in classification [24,48] using the confusion matrix. In the following formula, True Negative refers to correctly rejected samples, True Positive (TP) refers to correctly identified samples, False Positive refers to incorrectly identified samples and False Negative (FN) means incorrectly rejected samples:…”

Computational of Concrete Slump Model Based on H2O Deep Learning framework and Bagging to reduce Effects of Noise and Overfitting

“…H2O works better than RNN for transactional data because RNN is strong in sequential or time series data [22]. To improve performance of the model, ensemble approaches like Random Forests and Gradient Boosting Regression Trees [23], and Bagging [18], Smoothed Bootstrap Resampling [26], [27] could be used to reduce the negative effect of inherent noise [28] and overfitting. Li states that Random Forests and Gradient Boosting Regression Trees do not improve the prediction quality [23].…”