The -nearest neighbor classification method (-NNC) is one of the simplest nonparametric classification methods. The mutual -NN classification method (MNNC) is a variant of -NNC based on mutual neighborship. We propose another variant of -NNC, the symmetric -NN classification method (SNNC) based …
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48 results for “k-NN classification”
Paper proposes MS-k-NN for improved convergence rate in k-NN classification.
problem Improving convergence rate of k-NN classification methods.
method Proposes MS-k-NN that extrapolates unweighted k-NN estimators to k=0.
result MS-k-NN achieves improved convergence rate under certain conditions.
Multiple classifier systems focus on the combination of classifiers to obtain better performance than a single robust one. These systems unfold three major phases: pool generation, selection and integration. One of the most promising MCS approaches is Dynamic Selection (DS), which relies on finding the most competent c…
A new method for learning distance metrics for K-NN classification.
problem Improving the performance of K-NN classifier by learning an appropriate distance metric.
method Designing a continuous decision function for K-NN and minimizing its continuous empirical risk function.
result The proposed ANN algorithm outperforms existing methods like LMNN, NCA, and pairwise constraints.
Implementing -NN classification using Gromov--Wasserstein distances
problem Comparing metric measure spaces
method Gromov--Wasserstein and fused Gromov--Wasserstein distances
result Universal consistency of -NN classifiers
Combines k-NN and RVM for improved classification accuracy.
problem Improving k-NN's performance by considering relevancy.
method Integrates k-NN and RVM in kernel space, introduces a new stopping parameter.
result Significantly prunes irrelevant attributes and improves classification accuracy.
Improved OOD detection using label smoothing and k-NN density estimates.
problem Detecting out-of-distribution examples in classification models.
method Label smoothing and k-NN density estimate on intermediate activations.
result Label smoothing improves OOD detection performance, both theoretically and empirically.
The problem of supervised classification (or discrimination) with functional data is considered, with a special interest on the popular k-nearest neighbors (k-NN) classifier. First, relying on a recent result by Cerou and Guyader (2006), we prove the consistency of the k-NN classifier for functional data whose distribu…
The paper optimizes k-NN for distributed learning with minimax optimal performance.
problem Minimizing error rates in classification, regression, and density estimation.
method Optimal aggregation of fixed-k nearest neighbors from multiple subsets of data.
result Achieves minimax optimal error rates up to a logarithmic factor.
We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's prediction for a given test example depends on the labels of the training examples in its local neighborhood. This motivates us to derive a r…
Proposes LRR and LRLR for improving stock prediction accuracy.
problem Improving stock prediction accuracy through nonparametric classification.
method Local radial regression and logistic regression variant.
result LRLR outperforms LPoR and MS--NN in real-world stock datasets.
The -nearest neighbour (-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the -NN classifier. In this thesis we investigate the use of alternative distances for the -NN classifier. We …
Adaptive k-NN classifier improves accuracy over fixed k-NN.
problem Improving classification accuracy by dynamically choosing k.
method Adaptive selection of k based on local neighborhood properties.
result The adaptive k-NN classifier performs comparably to or better than fixed k-NN.
Universal consistency and rates of convergence of multiclass prototype algorithms in metric spacescs.LG
Prototype rules simplify multiclass classification in metric spaces, achieving consistency and reduced complexity.
problem Multiclass classification in metric spaces, focusing on universal consistency and convergence rates.
method Novel Proto-NN and hybrid rules for multiclass classification in metric spaces, analyzing convergence rates.
result Proto-NN is universally consistent and simpler to implement, with similar computational advantages.
New method learns local metrics for k-NN classification using sample similarity.
problem Improving k-NN classification accuracy through better distance metrics.
method Local distance metric learning based on sample similarity, using conical combinations of metric weight matrices.
result New metrics yield smaller distances for similar samples and larger distances for dissimilar ones.
In this paper, we consider the problem of malware detection and classification based on image analysis. We convert executable files to images and apply image recognition using deep learning (DL) models. To train these models, we employ transfer learning based on existing DL models that have been pre-trained on massive …
Improves k-NN for monotonic data with robustness against noise.
problem Class noise in real-life data violates monotonic constraints in k-NN.
method Monotonic Fuzzy k-NN (MonFkNN) with new fuzzy membership calculation.
result Significant accuracy improvements and robustness against monotonic noise.
Under-bagging -NN improves performance on imbalanced classification.
problem Imbalanced classification problems where one class is significantly underrepresented.
method Proposes an under-bagging -NN ensemble learning algorithm, analyzing convergence rates and efficiency.
result Achieves optimal convergence rates under mild assumptions and reduces sub-sample size and for highly imbalanced data.
This paper studies the relationship between the classification performed by deep neural networks (DNNs) and the decision of various classical classifiers, namely k-nearest neighbours (k-NN), support vector machines (SVM) and logistic regression (LR), at various layers of the network. This comparison provides us with ne…
Paper develops robust -NN algorithm for few samples.
problem Learning robust classifier from limited samples.
method Distributionally robust formulation of weighted -NN.
result Robust classifier improves generalization with smaller Lipschitz norm.
A new robust time series distance metric for k-NN classification.
problem Robustness against arbitrary data contamination in time series classification.
method Proposes a novel distance metric with worst-case complexity.
result Demonstrates competitive classification accuracy in k-NN time series classification.
Nearest Neighbors (NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we pro…
This paper proposes an inexpensive way to learn an effective dissimilarity function to be used for -nearest neighbor (-NN) classification. Unlike Mahalanobis metric learning methods that map both query (unlabeled) objects and labeled objects to new coordinates by a single transformation, our method learns a trans…
A new -NN algorithm using surprisal for robust and interpretable nonparametric learning.
problem Complex patterns and relationships in data without strong distribution assumptions.
method Surprisal-driven -NN framework for classification, regression, density estimation, and anomaly detection.
result State-of-the-art results in classification and anomaly detection, competitive regression results.
Proposes an online metric learning method for multi-label classification.
problem Lack of consideration for label dependencies and theoretical analysis of loss functions in existing multi-label classification methods.
method Develops a novel online metric learning paradigm based on k-Nearest Neighbour (kNN) and large margin principle, adapted for online streaming data.
result The proposed OML algorithm outperforms state-of-the-art methods on benchmark multi-label datasets.
Social messages classification is a research domain that has attracted the attention of many researchers in these last years. Indeed, the social message is different from ordinary text because it has some special characteristics like its shortness. Then the development of new approaches for the processing of the social…
In the -nearest neighborhood model (-NN), we are given a set of points , and we shall answer queries by returning the nearest neighbors of in according to some metric. This concept is crucial in many areas of data analysis and data processing, e.g., computer vision, document retrieval and machi…
We propose a procedure for supervised classification that is based on potential functions. The potential of a class is defined as a kernel density estimate multiplied by the class's prior probability. The method transforms the data to a potential-potential (pot-pot) plot, where each data point is mapped to a vector of …
We derive high-probability finite-sample uniform rates of consistency for -NN regression that are optimal up to logarithmic factors under mild assumptions. We moreover show that -NN regression adapts to an unknown lower intrinsic dimension automatically. We then apply the -NN regression rates to establish new …
Method detects adversarial samples using influence functions and nearest neighbors.
problem Detecting adversarial attacks on deep neural networks.
method Influence functions and k-NN model on activation layers.
result Successfully distinguishes adversarial examples with state-of-the-art results.
Enhances k-NN accuracy through randomized hyperstructure.
problem Improves k-NN accuracy by optimizing neighbor selection.
method Constructs a random n-dimensional hyperstructure around test instances to refine neighbor selection.
result 85.71% accuracy on Haberman's Cancer Survival dataset, compared to 80.95% for conventional k-NN.
A fast method for LOOCV in k-NN regression reduces computation time.
problem Efficient computation of LOOCV for k-NN regression.
method Identical LOOCV estimate to (k+1)-NN MSE on training data.
result LOOCV computation can be done with (k+1)-NN regression once.
Paper studies transfer learning for nonparametric classification, establishing rates and proposing adaptive classifiers.
problem Transfer learning in nonparametric classification under different distributions.
method Established minimax rates and proposed adaptive classifiers based on weighted K-NN approach.
result Data-driven adaptive classifier achieves near-optimal rates over various parameter spaces.
Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.
problem Noise and pseudo-neighbours in large-scale databases affect k-NN performance.
method Introduces a weighted mutual k-Nearest Neighbour algorithm to detect and remove noise, and minimize distant neighbours' influence.
result The proposed algorithm provides comparative better results compared to standard k-NN.
From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on genetic variations at the DNA base pair level, called Single-Nucleotide Polymorphisms (SNPs), collected from the Ontario Heart Genomics Study (OHGS). First, the thesis explains two commonly used supervised le…
Study shows -NN classifier is not universally consistent on but consistent on discrete and specific measure spaces.
problem Consistency of -NN classifier under Wasserstein distance on measure spaces.
method Analysis of -NN classifier properties under Wasserstein distance, use of -finite metric dimension, geodesic structures of Wasserstein spaces.
result Consistency of -NN classifier on specific measure spaces (discrete, Gaussian, wavelet series) but not on .
A novel k-NN method estimates conditional mean and variance efficiently.
problem Joint estimation of conditional mean and variance.
method Integrates k-NN with automated variance selection.
result Achieves fast convergence rates and improved precision.
The -NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct -NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to const…
We propose a simple approach which, given distributed computing resources, can nearly achieve the accuracy of -NN prediction, while matching (or improving) the faster prediction time of -NN. The approach consists of aggregating denoised -NN predictors over a small number of distributed subsamples. We show, bot…
Triplet networks are widely used models that are characterized by good performance in classification and retrieval tasks. In this work we propose to train a triplet network by putting it as the discriminator in Generative Adversarial Nets (GANs). We make use of the good capability of representation learning of the disc…
Proposes LRVM for better pattern classification of distant test instances.
problem Poor prediction for distant test instances in RVM.
method Combines RVM and k-NN to locally classify test instances.
result LRVM performs competitively compared to state-of-the-art classifiers.
Simple k-NN filtering improves model accuracy on noisy labels.
problem Training models with noisy labels reduces performance and is hard to identify.
method A simple -nearest neighbor-based filtering approach on the logit layer.
result Improves model accuracy compared to recent methods.
Improved convergence rate for kNN graph Laplacians with adaptive bandwidth.
problem Enhancing the efficiency of graph-based data analysis methods.
method Introducing a new class of kNN graph with adaptive bandwidth and proving operator convergence rate.
result Operator convergence rate of for the kNN graph Laplacian, up to a log factor.
Learning from many real-world datasets is limited by a problem called the class imbalance problem. A dataset is imbalanced when one class (the majority class) has significantly more samples than the other class (the minority class). Such datasets cause typical machine learning algorithms to perform poorly on the classi…
Paper proposes WGAIN for missing feature reconstruction.
problem Missing data in datasets.
method Wasserstein Generative Adversarial Imputation Network (WGAIN) compared to traditional methods.
result WGAIN outperforms traditional methods in imputing missing data.
Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for trai…
The paper introduces methods to solve optimization problems with auxiliary data.
problem Solving multistage optimization problems with uncertain data and auxiliary information.
method Utilizes machine learning techniques like kNN, CART, and RF to develop methods for optimization.
result Demonstrates asymptotic and finite sample optimality of the proposed methods.
Estimating entropy and mutual information consistently is important for many machine learning applications. The Kozachenko-Leonenko (KL) estimator (Kozachenko & Leonenko, 1987) is a widely used nonparametric estimator for the entropy of multivariate continuous random variables, as well as the basis of the mutual inform…