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Undersampling based on generalized learning vector quantization and natural nearest neighbors for imbalanced data
Imbalanced datasets can adversely affect classifier performance. Conventional undersampling approaches may lead to the loss of essential information,...
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A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation
The k -nearest neighbors ( k -NN) is a basic machine learning (ML) algorithm, and several quantum versions of it, employing different distance metrics,...
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Musical Instrument Classification Using k-Nearest Neighbors
This work presents the use of the k-nearest neighbors (kNNs) technique to achieve the classification of selected sounds through the use of audio...
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Classification of the Ionospheric Disturbances Caused by Geomagnetic and Seismic Activity with K-Nearest Neighbors Algorithm
Detection of earthquake-precursor signals a few days before the earthquake day has become an area of increasing interest. In recent years, it has...
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PDR-SMOTE: an imbalanced data processing method based on data region partition and K nearest neighbors
With the development and progress of machine learning, classification algorithms are commonly used. One of the main factors that affect...
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Unsupervised image clustering algorithm based on contrastive learning and K-nearest neighbors
With the development of the times, people generate a huge amount of data every day, most of which are unlabeled data, but manual labeling needs a lot...
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A robust method based on locality sensitive hashing for K-nearest neighbors searching
K-nearest neighbors searching (KNNS) is to find K -nearest neighbors for query points. It is a primary problem in clustering analysis, classification,...
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Innovative compressive strength prediction for recycled aggregate/concrete using K-nearest neighbors and meta-heuristic optimization approaches
This paper presents a groundbreaking method for predicting the compressive strength ( F c ) of recycled aggregate concrete (RAC) through the application...
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Reusability Analysis of K-Nearest Neighbors Variants for Classification Models
The K-nearest neighbors (KNN) algorithm is a powerful example-based method that is easy to implement in classification tasks. As a result, in the...
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Development of rapidly exploring random tree based autonomous mobile robot navigation and velocity predictions using K-nearest neighbors with fuzzy logic analysis
The purpose of autonomous mobile robot navigation is to construct the optimal defended path. In order to ameliorate the accuracy of real time...
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A new sample reduction method for decreasing the running time of the k-nearest neighbors algorithm to diagnose patients with congestive heart failure: backward iterative elimination
The model complexity is strictly connected to both the sample size and the number of features in a conventional pattern recognition study. Although...
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Comparison of Convolutional Neural Networks and K-Nearest Neighbors for Music Instrument Recognition
Music instrument recognition is one of the main tasks of music information retrieval. Identification of instruments present in an audio track...
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Density Peaks Clustering Algorithm Based on K Nearest Neighbors
Density peaks clustering algorithms calculate the local density based on the cutoff distance and the global distribution of the sample. They cannot...
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Application of Quantum-Based K-Nearest Neighbors Algorithm in Optical Fiber Classification
Due to the various features of fiber-optic cables, the fields of use vary from one type of cable to another. Therefore, identifying the cable is...
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A novel density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy
Recently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster...
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Semantic segmentation of large-scale point clouds based on dilated nearest neighbors graph
Three-dimensional (3D) semantic segmentation of point clouds is important in many scenarios, such as automatic driving, robotic navigation, while...
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A Clustering Method Based on Improved Density Estimation and Shared Nearest Neighbors
Density-based clustering methods can detect clusters of arbitrary shapes. Most traditional clustering methods need the number of clusters to be given...
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Early Stage Parkinson’s Disease Diagnosis and Detection Using K-Nearest Neighbors Algorithm
This research article proposes a novel method to detect and recognize the symptoms of Parkinson’s disease in its early stages. Parkinson’s disease...
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Surface roughness prediction in micro-plasma transferred arc metal additive manufacturing process using K-nearest neighbors algorithm
Micro-plasma transfer arc metal additive manufacturing (μ-PTAMAM) process is a unique direct energy deposition (DED) type additive manufacturing (AM)...
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A feature weighted K-nearest neighbor algorithm based on association rules
K-nearest neighbors (kNN) is a popular machine learning algorithm because of its clarity, simplicity, and efficacy. kNN has numerous drawbacks,...
