A recent analysis in Health Services Research and Managerial Epidemiology suggests that machine learning can play a role in helping predict readmissions for atrial fibrillation (AFib). The authors used data from the 2013 Nationwide Readmissions Database on AFib, aiming to create risk prediction models and ultimately predict 90-day hospital readmission rates. The researchers employed multiple machine learning methods (k-Nearest Neighbors, Decision Tree, and Support Vector Machine) to determine variable importance. The average patient age was 64.9 years, with 62% of patients being male. The primary outcome of interest was 90-day hospital readmissions status.

Using prototype methods to reduce the size of training datasets can drastically reduce the computational cost of classification with instance-based learning algorithms like the k-Nearest Neighbour classifier. The number and distribution of prototypes required for the classifier to match its original performance is intimately related to the geometry of the training data. As a result, it is often difficult to find the optimal prototypes for a given dataset, and heuristic algorithms are used instead. However, we consider a particularly challenging setting where commonly used heuristic algorithms fail to find suitable prototypes and show that the optimal prototypes can instead be found analytically. We also propose an algorithm for finding nearly-optimal prototypes in this setting, and use it to empirically validate the theoretical results.

Machine learning (ML) algorithms are often categorized as either supervised or unsupervised, and this broadly refers to whether the dataset being used is labelled or not. Supervised ML algorithms apply what has been learned in the past to new data by using labelled examples to predict future outcomes. Essentially, the correct answer is known for these types of problems and the estimated model's performance is judged based on whether or not the predicted output is correct. In contrast, unsupervised ML algorithms refer to those developed when the information used to train the model is neither classified nor labelled. These algorithms work by attempting to make sense out of data by extracting features and patterns that can be found within the sample.

K-Nearest-Neighbors (KNN) graphs are central to many emblematic data mining and machine-learning applications. Some of the most efficient KNN graph algorithms are incremental and local: they start from a random graph, which they incrementally improve by traversing neighbors-of-neighbors links. Paradoxically, this random start is also one of the key weaknesses of these algorithms: nodes are initially connected to dissimilar neighbors, that lie far away according to the similarity metric. As a result, incremental algorithms must first laboriously explore spurious potential neighbors before they can identify similar nodes, and start converging. In this paper, we remove this drawback with Cluster-and-Conquer (C 2 for short).

Radius Neighbors Classifier is a classification machine learning algorithm. It is an extension to the k-nearest neighbors algorithm that makes predictions using all examples in the radius of a new example rather than the k-closest neighbors. As such, the radius-based approach to selecting neighbors is more appropriate for sparse data, preventing examples that are far away in the feature space from contributing to a prediction. In this tutorial, you will discover the Radius Neighbors Classifier classification machine learning algorithm. Radius Neighbors Classifier Algorithm With Python Photo by J. Triepke, some rights reserved. Radius Neighbors is a classification machine learning algorithm.

Learning a robust classifier from a few samples remains a key challenge in machine learning. A major thrust of research in classification with few training samples has been based on metric learning to capture similarities between samples and then perform the $k$-nearest neighbor algorithm. To make such an algorithm more robust, in this paper, we propose a distributionally robust $k$-nearest neighbor algorithm Dr.k-NN, which features assigning minimax optimal weights to training samples when performing classification. We also couple it with neural-network-based feature embedding. We demonstrate the competitive performance of our algorithm comparing to the state-of-the-art in the few-training-sample setting with various real-data experiments.

The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric machine learning algorithm. However, it is well known that it suffers from the curse of dimensionality, which is why in practice one often applies a kNN classifier on top of a (pre-trained) feature transformation. From a theoretical perspective, most, if not all theoretical results aimed at understanding the kNN classifier are derived for the raw feature space. This leads to an emerging gap between our theoretical understanding of kNN and its practical applications. In this paper, we take a first step towards bridging this gap. We provide a novel analysis on the convergence rates of a kNN classifier over transformed features. This analysis requires in-depth understanding of the properties that connect both the transformed space and the raw feature space. More precisely, we build our convergence bound upon two key properties of the transformed space: (1) safety -- how well can one recover the raw posterior from the transformed space, and (2) smoothness -- how complex this recovery function is. Based on our result, we are able to explain why some (pre-trained) feature transformations are better suited for a kNN classifier than other. We empirically validate that both properties have an impact on the kNN convergence on 30 feature transformations with 6 benchmark datasets spanning from the vision to the text domain.

The work reported here addresses the capacity of psychophysiological sensors and measures using Electroencephalogram (EEG) and Galvanic Skin Response (GSR) to detect levels of trust for humans using AI-supported Human-Machine Interaction (HMI). Improvements to the analysis of EEG and GSR data may create models that perform as well, or better than, traditional tools. A challenge to analyzing the EEG and GSR data is the large amount of training data required due to a large number of variables in the measurements. Researchers have routinely used standard machine-learning classifiers like artificial neural networks (ANN), support vector machines (SVM), and K-nearest neighbors (KNN). Traditionally, these have provided few insights into which features of the EEG and GSR data facilitate the more and least accurate predictions - thus making it harder to improve the HMI and human-machine trust relationship. A key ingredient to applying trust-sensor research results to practical situations and monitoring trust in work environments is the understanding of which key features are contributing to trust and then reducing the amount of data needed for practical applications. We used the Local Interpretable Model-agnostic Explanations (LIME) model as a process to reduce the volume of data required to monitor and enhance trust in HMI systems - a technology that could be valuable for governmental and public sector applications. Explainable AI can make HMI systems transparent and promote trust. From customer service in government agencies and community-level non-profit public service organizations to national military and cybersecurity institutions, many public sector organizations are increasingly concerned to have effective and ethical HMI with services that are trustworthy, unbiased, and free of unintended negative consequences.

What these results have in common is that changes that either are imperceptible or should be irrelevant to the classification task can lead to drastically different network behavior. One reason for this vulnerability to adversarial attack is the non-Lipschitzness property of typical neural networks: small but adversarial movements in the input space can often produce large perturbations in the feature space. In this work, we consider the question of whether non-Lipschitz networks are intrinsically vulnerable, or if they could still be made robust to adversarial attack, in an abstract but (we believe) instructive adversarial model. In particular, suppose an adversary, by making an imperceptible change to an input x, can cause its representation F (x) in feature space (the penultimate layer of the network) to move by an arbitrary amount: will such an adversary always win? Clearly if the adversary can modify F (x) by an arbitrary amount in an arbitrary direction, then yes. But what if the adversary can modify F (x) by an arbitrary amount but only in a random direction (which it cannot control)? In this case, we show an interesting dichotomy: if the classifier must output a classification on any input it is given, then yes the adversary will still win, no matter how well-separated the classes are in feature space and no matter what decision surface the classifier uses.

We propose a Similarity-Based Stratified Splitting (SBSS) technique, which uses both the output and input space information to split the data. The splits are generated using similarity functions among samples to place similar samples in different splits. This approach allows for a better representation of the data in the training phase. This strategy leads to a more realistic performance estimation when used in real-world applications. We evaluate our proposal in twenty-two benchmark datasets with classifiers such as Multi-Layer Perceptron, Support Vector Machine, Random Forest and K-Nearest Neighbors, and five similarity functions Cityblock, Chebyshev, Cosine, Correlation, and Euclidean. According to the Wilcoxon Sign-Rank test, our approach consistently outperformed ordinary stratified 10-fold cross-validation in 75\% of the assessed scenarios.