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Early Prediction of Learners At-Risk of Failure in Online Professional Training Using a Weighted Vote
Professional training involves the acquisition of knowledge, skills, and expertise required to perform specific job roles. It can take various forms,...
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RAM: resource allocation in MIMO–MISO cognitive IoT for 5G wireless networks using two-level weighted majority cooperative game
Cognitive Internet of things (CIoT) is the solution for resource allocation problem in an exponentially increasing number of Internet of things in...
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A Split-Then-Join Lightweight Hybrid Majority Vote Classifier
Classification of human activities using smallest dataset is achievable with tree-oriented (C4.5, Random Forest, Bagging) algorithms. However, the...
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An improved KNN classifier based on a novel weighted voting function and adaptive k-value selection
This paper presents a modified KNN classifier (HMAKNN) based on the harmonic mean of the vote and average distance of the neighbors of each class...
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Online GBDT with Chunk Dynamic Weighted Majority Learners for Noisy and Drifting Data Streams
In the field of data mining, data stream mining has become one of the research focuses, in which noise and concept drift are two main challenges....
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Ensemble learning with weighted voting classifier for melanoma diagnosis
Melanoma, the most lethal type of skin cancer, presents a substantial public health challenge. Detecting melanoma promptly is paramount for enhancing...
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A Lightweight Hybrid Majority Vote Classifier Using Top-k Dataset
Human activity recognition on resource constrained device such as Smartphone is possible using small dataset. In this paper, we present our unique...
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On the Graph Theory of Majority Illusions
The popularity of an opinion in one’s direct circles is not necessarily a good indicator of its popularity in one’s entire community. For instance,...
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Spiking Neural P System with weight model of majority voting technique for reliable interactive image segmentation
Interactive image segmentation is a method for precisely segmenting of the object from background using information entered by the user. However,...
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An attribute-weighted isometric embedding method for categorical encoding on mixed data
Mixed data containing categorical and numerical attributes are widely available in real-world. Before analysing such data, it is typically necessary...
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Unveiling the silent majority: stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networks
Social Media (SM) has become a popular medium for individuals to share their opinions on various topics, including politics, social issues, and daily...
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Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound
In the PAC-Bayesian literature, the C-Bound refers to an insightful relation between the risk of a majority vote classifier (under the zero-one loss)...
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Vote-based integration of review spam detection algorithms
Due to the growth of online review data, detecting fake or fraudulent reviews is becoming an urgent issue. One barrier to effective detection of fake...
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A robust combined weighted label fusion in multi-atlas pancreas segmentation
Multi-atlas segmentation frameworks have proved to be a top-method as its good performance in medical image segmentation, which mainly consists of...
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The classification of medical and botanical data through majority voting using artificial neural network
Data classification has many approaches in data mining and machine learning. The artificial neural network (ANN) is applied to classify the data that...
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Imbalanced instance selection based on Laplacian matrix decomposition with weighted k-nearest-neighbor graph
Data are an essential component for building machine learning models. Linearly separable high-quality data are conducive to building efficient...
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Human fall detection using neuro-fuzzy models based on ensemble learning
Human falling may be due to a violent act, a heart attack or perhaps physical illness. Every year, many old people are being treated for injuries or...
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DCA-Based Weighted Bagging: A New Ensemble Learning Approach
Ensemble learning is a highly efficient method that combines multiple machine learning models to improve the accuracy and robustness of predictions....
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HEDL-IDS2: An Innovative Hybrid Ensemble Deep Learning Prototype for Cyber Intrusion Detection
The growing volume of online activities exposes users to potential cyber-attacks. Consequently, the scientific community aims to develop pioneering...
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An optimization approach with weighted SCiForest and weighted Hausdorff distance for noise data and redundant data
With the development of intelligent technology, data obtained from practical applications may be subject to noise information (outlier data or...
