Ensemble learning combines a series of base classifiers and the final result is assigned to the corresponding class by using a majority voting mechanism. However, the base classifiers in the ensemble cannot perform equally well. Therefore, the base classifiers should be assigned different weights in order to increase classification performance. In this study, a novel Weighted Majority Voting Ensemble (WMVE) approach is proposed, which evaluates individual performances of each classifier in the ensemble and adjusts their contributions to class decision. In the proposed weight adjustment model, only reward mechanism is provided, so punishment is not included. Classifiers that correctly classify observations which are not correctly classified by most of the classifiers gain more weights in the ensemble. In the experimental studies, increasing value of weight was calculated for each classifier in a heterogeneous collection of classification algorithms, including C4.5 decision tree, support vector machine, k-nearest neighbor, k-star, and naive Bayes. The proposed method (WMVE) was compared with the Simple Majority Voting Ensemble (SMVE) approach in terms of classification accuracy on 28 benchmark datasets. The effectiveness of the proposed method is confirmed by the experimental results.

As the concept of Industry 4.0 is introduced, artificial intelligence-based fault analysis is attracted the corresponding community to develop effective intelligent fault diagnosis and prognosis (IFDP) models for rotating machinery. Hence, various challenges arise regarding model assessment, suitability for real-world applications, fault-specific model development, compound fault existence, domain adaptability, data source, data acquisition, data fusion, algorithm selection, and optimization. It is essential to resolve those challenges for each component of the rotating machinery since each issue of each part has a unique impact on the vital indicators of a machine. Based on these major obstacles, this study proposes a comprehensive review regarding IFDP procedures of rotating machinery by minding all the challenges given above for the first time. In this study, the developed IFDP approaches are reviewed regarding the pursued fault analysis strategies, considered data sources, data types, data fusion techniques, machine learning techniques within the frame of the fault type, and compound faults that occurred in components such as bearings, gear, rotor, stator, shaft, and other parts. The challenges and future directions are presented from the perspective of recent literature and the necessities concerning the IFDP of rotating machinery.

The k-nearest neighbors (KNN) algorithm has been widely used for classification analysis in machine learning. However, it suffers from noise samples that reduce its classification ability and therefore prediction accuracy. This article introduces the high-level k-nearest neighbors (HLKNN) method, a new technique for enhancing the k-nearest neighbors algorithm, which can effectively address the noise problem and contribute to improving the classification performance of KNN. Instead of only considering k neighbors of a given query instance, it also takes into account the neighbors of these neighbors. Experiments were conducted on 32 well-known popular datasets. The results showed that the proposed HLKNN method outperformed the standard KNN method with average accuracy values of 81.01% and 79.76%, respectively. In addition, the experiments demonstrated the superiority of HLKNN over previous KNN variants in terms of the accuracy metric in various datasets.