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About 10,000 papers for “binary classification”

Optimal Binary Classification Beyond Accuracy

Shashank Kumar Singh, Justin Khim · arXiv (Cornell University) · 2021

The vast majority of statistical theory on binary classification characterizes performance in terms of accuracy. However, accuracy is known in many cases to poorly reflect the practical consequences of classification error, most famously in imbalanced binary classification, where data are dominated by samples from one of two classes. The first part of this paper derives a novel generalization of the Bayes-optimal classifier from accuracy to any performance metric computed from the confusion matrix. Specifically, this result (a) demonstrates that stochastic classifiers sometimes outperform the best possible deterministic classifier and (b) removes an empirically unverifiable absolute continuity assumption that is poorly understood but pervades existing results. We then demonstrate how to use this generalized Bayes classifier to obtain regret bounds in terms of the error of estimating regression functions under uniform loss. Finally, we use these results to develop some of the first finite-sample statistical guarantees specific to imbalanced binary classification. Specifically, we demonstrate that optimal classification performance depends on properties of class imbalance, such as a novel notion called Uniform Class Imbalance, that have not previously been formalized. We further illustrate these contributions numerically in the case of $k$-nearest neighbor classification

Cited by 4Open access

ROC curve for binary classification.

Nicodemus Songose Awarayi (18414494), Frimpong Twum (14593669), James Ben Hayfron-Acquah (17507473) et al. · Figshare · 2024

<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer’s disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer’s disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer’s disease versus normal controls, Alzheimer’s disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.</p></div>

Cited by 0Open access

Binary classification and related tasks

Peter Flach · Machine Learning · 2012

IN THIS CHAPTER and the next we take a bird's-eye view of the wide range of different tasks that can be solved with machine learning techniques. ‘Task’ here refers to whatever it is that machine learning is intended to improve performance of (recall the definition of machine learning on p.3), for example, e-mail spam recognition. Since this is a classification task, we need to learn an appropriate classifier from training data. Many different types of classifiers exist: linear classifiers, Bayesian classifiers, distancebased classifiers, to name a few. We will refer to these different types as models; they are the subject of Chapters 4–9. Classification is just one of a range of possible tasks for which we can learn a model: other tasks that will pass the review in this chapter are class probability estimation and ranking. In the next chapter we will discuss regression, clustering and descriptive modelling. For each of these tasks we will discuss what it is, what variants exist, how performance at the task could be assessed, and how it relates to other tasks. We will start with some general notation that is used in this chapter and throughout the book (see Background 2.1 for the relevant mathematical concepts).

Cited by 0

Confusion matrix for binary classification.

Nicodemus Songose Awarayi (18414494), Frimpong Twum (14593669), James Ben Hayfron-Acquah (17507473) et al. · Figshare · 2024

<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer’s disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer’s disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer’s disease versus normal controls, Alzheimer’s disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.</p></div>

Cited by 0Open access

Binary classification results on ZI.

Ja-Ho Koo (6561596), Ali Moradvandi (21300790), Edo Abraham (6677921) et al. · Figshare · 2025

<div> Effective reservoir flood control demands real-time decision-making that balances multiple objectives. However, traditional optimization approaches are often too computationally intensive and become intractable when considering dynamically changing preferences of operators, modelled as weights of different objectives. This study aims to develop tractable real-time flood control strategies that maintain performance while reducing computational complexity. We propose two data-driven approaches based on Model Predictive Control (MPC): (1) an explicit MPC using deep neural networks to directly determine optimal outflow schedules, and (2) a switched MPC that produces optimal weights of objectives based on hydrological conditions. Both methods leverage offline learning from an online Parameterized Dynamic MPC framework incorporating state-dependent weights. We tested these approaches on South Korea’s Daecheong multipurpose reservoir using historical flood events with various patterns. The explicit MPC demonstrated reliable performance under conditions similar to its training data. However, it showed frequent changes in outflow schedules and constraint violations for scenarios outside training data. In contrast, the switched MPC maintained robustness across all test scenarios due to a linear optimization process in a receding horizon manner, though with slightly reduced performance compared to the explicit MPC under scenarios inside the range of training data. Most significantly, both approaches reduced computation time from approximately 10 minutes to less than one second, making real-time implementation feasible. This dramatic improvement enables prompt decision-making during rapidly evolving flood events while maintaining near-optimal control performance. </div>

Cited by 0Open access

Classification of binary random patterns

K. Abend, Thomas J. Harley, Laveen N. Kanal · IEEE Transactions on Information Theory · 1965

In various pattern-recognition problems such as classification of photographic data, preprocessing operations result in a two-dimensional array of binary random variables. An optimal recipe for classifying such patterns is described. It combines the use of an orthonormal expansion for the logarithm of probability functions, with the generation of a joint probability distribution from lower-order marginals. The unwieldy nature of the optimal recipe leads to the consideration of dependence represented by a Markov chain and a two-dimensional analog called a Markov mesh. The Markov chain has a "reflecting" property in that the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</tex> th order nonstationary chain implies that a point depends on its <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</tex> nearest neighbors on each side. By scanning along a grid-filling curve so that the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2r</tex> neighbors of a point are geometrically close to it, certain spatial dependencies are obtained. The grid-filling curves are sequences of functions which have Hilbert "space-filling" curves as their limit. This simple approach does not provide for many of the spatial dependencies that should be considered. A novel extension of Markov chain methods into two dimensions leads to the Markov mesh which economically takes care of a much larger class of spatial dependencies. Likelihood ratio classification based on Markov chain and Markov mesh assumptions requires the estimation of a much smaller number of parameters than the general case. The development presented in this paper is new and need not be restricted to binary variables.

Cited by 249

Cautious Calibration in Binary Classification

Mari-Liis Allikivi, Joonas Järve, Meelis Kull · Frontiers in artificial intelligence and applications · 2024

Being cautious is crucial for enhancing the trustworthiness of machine learning systems integrated into decision-making pipelines. Although calibrated probabilities help in optimal decision-making, perfect calibration remains unattainable, leading to estimates that fluctuate between under- and overconfidence. This becomes a critical issue in high-risk scenarios, where even occasional overestimation can lead to extreme expected costs. In these scenarios, it is important for each predicted probability to lean towards underconfidence, rather than just achieving an average balance. In this study, we introduce the novel concept of cautious calibration in binary classification. This approach aims to produce probability estimates that are intentionally underconfident for each predicted probability. We highlight the importance of this approach in a high-risk scenario and propose a theoretically grounded method for learning cautious calibration maps. Through experiments, we explore and compare our method to various approaches, including methods originally not devised for cautious calibration but applicable in this context. We show that our approach is the most consistent in providing cautious estimates. Our work establishes a strong baseline for further developments in this novel framework.

Cited by 0Open access

Simple Classification using Binary Data

Deanna Needell, Rayan Saab, Tina Woolf · arXiv (Cornell University) · 2017

Binary, or one-bit, representations of data arise naturally in many applications, and are appealing in both hardware implementations and algorithm design. In this work, we study the problem of data classification from binary data and propose a framework with low computation and resource costs. We illustrate the utility of the proposed approach through stylized and realistic numerical experiments, and provide a theoretical analysis for a simple case. We hope that our framework and analysis will serve as a foundation for studying similar types of approaches.

Cited by 18Open access

Classification of binary covering codes

Patric R. J. �sterg�rd, William D. Weakley · Journal of Combinatorial Designs · 2000

The minimum size of a binary covering code of length n and covering radius r is denoted by K (n, r) and corresponding codes are called optimal. In this article a classification up to equivalence of all optimal covering codes having either length at most 8 or cardinality at most 4 is completed. Moreover, we prove that K (9, 2) = 16. © 2000 John Wiley & Sons, Inc. J Combin Designs 8: 391–401, 2000

Cited by 16

Data for binary classification experiments

Kattenbeck, Markus, Golab, Antonia, Alinaghi, Negar et al. · TU Wien Research Data · 2025

Research context This zip archive contains all the data and scripts which are neccessary to reproduce the results of the following paper, co-authored by Markus Kattenbeck, Ioannis Giannopoulos, Negar Alinaghi, Antonia Golab, and Daniel R. Montello: Predicting spatial familiarity by exploiting head and eye movements during pedestrian navigation in the real world This paper will be published in Springer Nature Scientific Reports. File overview The structure of the archive is the following: Folder "01_data" contains all the data files needed and a readme file describing the structure of each of these data files. These data files are: lsp.csv [contains demographic data about participants] matched_gaze_imu.csv [contains the segmented behavioral data, i.e. both gaze features and imu features] matched_gaze_imu_feature_description.pdf [contains a description of the features contained in matched_gaze_imu.csv] walking_dates.csv [contains an overview on which date participants walked the familiar and unfamiliar routes] users_polygons.csv [contains one or more polygons per participant in which they are familiar] polygons_markers.csv [contains locations of POIs per polygon for which participants reported to be familiar with] user_routes.csv [containes the route participants provided between a randomly selected pair of POIs they have provided for a given polygon] Folder "02_scripts" contains the data analysis scripts; they are organized in two subfolders: 01_ml_scripts: these are the scripts for the XGBoost classification; they are organized as two python files in which further instructions for use are given. 80_20_code.py is the python file which runs the ML experiments using an 80/20 train/test split L5O4T_code.py is the python file which runs the ML experiments leaving the full data of five different participants per condition as unseen data for the test. requirements.txt states the used Python package versions 02_r_scripts: cleaned_script.Rmd This is an R notebook which can be easily opened in R-Studio and provides the analysis scripts for the descriptive statistics presented in the paper. package_versions.txt states the used R package versions Licenses The code is licensed under MIT, the data is licensed under CC-BY.

Cited by 0Open access

Classification with binary hyperdimensional computing

Laura Smets · 2025

Hyperdimensional computing (HDC) has been introduced as a brain-inspired, lightweight, and energy-efficient algorithmic framework suitable for wearable internet of things (IoT) devices, near-sensor artificial intelligence (AI) applications, and on-device processing. It originates at the intersection of symbolic AI and connectionism, with the potential of combining the advantages of both, i.e., the transparency of symbolic representations, and flexibility and robustness of connectionist representations. HDC maps input data to vectors in a hyperdimensional (HD) space, i.e., hyperdimensional vectors (HVs) with typically thousands of components, and relies on simple component-wise arithmetic vector operations. Several frameworks of HDC exist, distinguished by their vector component types. This dissertation focuses on dense binary HDC since it has been reported to offer higher energy efficiency and more robustness compared to its non-binary counterpart, at the cost of a slightly lower classification accuracy. Therefore, this dissertation investigates adjustments and improvements to the dense binary HDC classification model to enhance its classification performance in terms of accuracy and transparency. First, a novel uniform framework is developed to encode binarized images into HVs. The framework uses a local feature extraction method relying only on native HD arithmetic vector operations. The proposed encoding framework outperforms other studies using native HDC with different encoding approaches, and is on par with more complex hybrid HDC models and lightweight binary neural networks (BNNs). It also demonstrates higher robustness to noise and blur compared to the baseline encoding. Second, two models that enhance previous HDC classifiers are introduced and explored. The models use a confidence metric that measures the confidence with which a sample has been classified during the training procedure. This enhanced training procedure consistently improves classification accuracy compared to the baseline and increases confidence in the classifier’s predictions. Third, a generic post-hoc analysis is proposed, which determines the importance of individual features in the HDC classification model. This analysis provides insight and transparency in the decision-making process at every deployment of the HDC classification model.

Cited by 0

Player Position Binary Classification Model

Kayanna A. Morgan, Rajesh Godasu, Emanuel S. Grant · 2024

This paper presents a binary classification model aimed at predicting whether a football player is best suited for an offensive or defensive position based on their skill set. The study addresses the challenges in precisely projecting a player’s future potential solely from their skills, a gap in sports analytics and player evaluation research. Leveraging machine learning techniques, specifically neural networks, the model aims to classify players into their optimal position groups, contributing to talent identification and recruitment strategies in football. Evaluation metrics demonstrate the model’s ability to accurately predict player positions, with a slight improvement observed in the enhanced model. However, the increase in model complexity does not yield significant performance gains, emphasizing the importance of balancing complexity with performance improvement.

Cited by 0