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About 10,000 papers for “binary classification”

Binary classification.

Soumadeep Saha (16813952), Utpal Garain (16813955), Arijit Ukil (14165014) et al. · Figshare · 2023

This file contains basic definitions and terminology associated with binary classification. (PDF)

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Binary classification.

Fleur Zeldenrust (4846177), Niccolò Calcini (18548192), Xuan Yan (4844139) et al. · Figshare · 2024

A) Receiver Operator Curve (ROC), where the hit rate was defined as the fraction of up-states, in which at least 1 action potential was fired. Similarly, the false alarm rate was defined as the fraction of down-states, in which at least 1 action potential was fired. In black the results for Poisson spike trains with firing rates matched to those of the experimental/simulation conditions are shown. B) Distribution of the number of spikes per period where the hidden state was 1 (up state), for inhibitory neurons (blue) and excitatory neurons (red). C) Same as B), but for periods where the hidden state was 0 (down state). D) Firing rate r distribution in the up-state. E) Firing rate r distribution in the down-state. F) Normalized firing rate rn distribution in the up-state G) Normalized firing rate rn distribution in the down-state. H) Delay (in ms) of each correct spike since the state switches from down to up. I) Delay (in ms) of each incorrect spike since the state switches from up to down. J) Normalized delay (delay/τ, unitless) of each correct spike since the state switches from down to up. K) Normalized delay of each incorrect spike since the state switch from up to down. The results of hypotheses test for A-F are in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012043#pcbi.1012043.s004" target="_blank">S2</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012043#pcbi.1012043.s005" target="_blank">S3</a> Tables. Data from 144 excitatory neurons (220 trials), 72 inhibitory neurons (78 trials), and 9 control inhibitory neurons (11 trials).

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Binary and Beyond Binary Classification

Kolla Bhanu Prakash · 2024

Binary and beyond binary classifications are mainly classified as follows: classification, scoring and ranking, class probability estimation, handling more than two classes, and descriptive learning. Descriptive learning theories make statements about how learning occurs and devise models that can be used to explain and predict learning results. Binary classification problems often require two classes—one representing the normal state and the other representing the aberrant state. One of the most often used machine learning algorithms, within the category of supervised learning, is logistic regression. The output of a classification algorithm is shown and summarized in a confusion matrix. The chapter covers the topics of how to assess multi-class performance and how to construct multi-class models from binary models. The difference between multi-class classification and multi-label classification is that the tasks in multi-label problems are all related in some way.

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Binary classification results.

Xiaoli Yang (314226), Jiayi Zhou (2228527), Chenchen Wang (12690) et al. · Figshare · 2025

<div> Alzheimer’s disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a key precursor. Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtle imaging differences. Furthermore, differentiating early MCI (EMCI) from late MCI (LMCI) is also important for interventions. This study proposes a deep learning-based approach using a weighted probability-based ensemble method to integrate results from three-dimensional residual networks (3D ResNet). (1) This study employs 3D ResNet-18, 3D ResNet-34, and 3D ResNet-50 architectures with the Convolutional Block Attention Module (CBAM). The attention mechanism enhances performance by helping the model focus on pertinent information. Data augmentation techniques are applied to address limited data and improve accuracy. (2) To overcome the limitation of the individual convolutional neural network (CNN), an ensemble learning method is adopted. The method assigns weights to each 3D CNN model based on prediction accuracy and integrates them to obtain the final result. Our method achieves accuracy of 94.87%, 92.31%, 95.49%, and 95.97% for MCI vs. NC, MCI vs. AD, EMCI vs. LMCI, and NC vs. EMCI vs. LMCI vs. AD, respectively. The results demonstrate the effectiveness of our method for AD diagnosis. </div>

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binary-surfactant-classification

Kitchin, John, Tilton, Robert, Chilkunda, Chetan · OPAL (Open@LaTrobe) (La Trobe University) · 2025

This data supports a manuscript titled "A Classification-based Methodology for the Estimation of Binary Surfactant Critical Micelle Concentrations" submitted to Digital Discovery. It contains data files and a Jupyter notebook that illustrates how to use the data to perform the analysis in the paper.

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Binary Classification Dataset

Kounadis, Gerasimos · Zenodo (CERN European Organization for Nuclear Research) · 2024

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Binary data classification.

Jean Daunizeau (195606), Vincent Adam (5657632), Lionel Rigoux (304110) · Figshare · 2015

This figure exemplifies a classification analysis, which is used to infer on the link between a continuous variable X and a binary data y. The analysis is conducted on data simulated under either a null model (H0: no link) or a sigmoid mapping (H1). Upper left: the classification accuracy, in terms of the Monte-Carlo average probability of correct prediction under both types of data (left: H1, right: H0), for the training dataset. The green dots show the expected classification accuracy, using the true values of each model's set of parameters. The dotted red line depicts chance level. Upper right: same format, test dataset (no model fitting). Lower left: same format, for the log Bayes factor , given the training dataset. Lower right: same format, given the full (train+test) dataset.

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VK binary classification

Firsanova, Victoria · Figshare · 2021

The dataset for binary text classification based on VK (russian social media) data.

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binary-surfactant-classification

Kitchin, John, Tilton, Robert, Chilkunda, Chetan · Figshare · 2025

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Mushroom Binary Classification

Carlos A. Hernández Alvarez · Zenodo (CERN European Organization for Nuclear Research) · 2024

&lt;p&gt;This Python notebook classifies mushrooms as either edible or poisonous using various machine learning models. The notebook demonstrates data loading, preprocessing, model training, and evaluation, aiming to serve as a foundational resource for similar classification projects in bioinformatics and other fields.&lt;/p&gt;\n&lt;h3&gt;Project Description&lt;/h3&gt;\n&lt;p&gt;This project applies machine learning techniques to classify mushrooms based on their physical characteristics. Key aspects include:&lt;/p&gt;\n&lt;ul&gt;\n&lt;li&gt;Loading and preprocessing the mushroom dataset.&lt;/li&gt;\n&lt;li&gt;Feature engineering and handling missing values.&lt;/li&gt;\n&lt;li&gt;Applying different machine learning models for classification.&lt;/li&gt;\n&lt;li&gt;Evaluating model performance using accuracy, recall, and F1-score.&lt;/li&gt;\n&lt;/ul&gt;\n&lt;h3&gt;Results&lt;/h3&gt;\n&lt;p&gt;The project provides metrics on the classification performance of different machine learning models, including Decision Tree, Random Forest, and XGBoost. These results help in understanding the effectiveness of various models in predicting the edibility of mushrooms.&lt;/p&gt;\n&lt;h3&gt;Conclusions&lt;/h3&gt;\n&lt;p&gt;The notebook successfully demonstrates the application of machine learning algorithms to classify mushrooms with high accuracy. This approach can be extended to other bioinformatics projects, such as disease classification, drug response prediction, and biomarker discovery. Continuous learning and exploration of machine learning techniques will enhance the ability to tackle complex problems in bioinformatics and beyond.&lt;/p&gt;

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Mushroom Binary Classification

Carlos A. Hernández Alvarez · Zenodo (CERN European Organization for Nuclear Research) · 2024

This Python notebook classifies mushrooms as either edible or poisonous using various machine learning models. The notebook demonstrates data loading, preprocessing, model training, and evaluation, aiming to serve as a foundational resource for similar classification projects in bioinformatics and other fields. Project Description This project applies machine learning techniques to classify mushrooms based on their physical characteristics. Key aspects include: Loading and preprocessing the mushroom dataset. Feature engineering and handling missing values. Applying different machine learning models for classification. Evaluating model performance using accuracy, recall, and F1-score. Results The project provides metrics on the classification performance of different machine learning models, including Decision Tree, Random Forest, and XGBoost. These results help in understanding the effectiveness of various models in predicting the edibility of mushrooms. Conclusions The notebook successfully demonstrates the application of machine learning algorithms to classify mushrooms with high accuracy. This approach can be extended to other bioinformatics projects, such as disease classification, drug response prediction, and biomarker discovery. Continuous learning and exploration of machine learning techniques will enhance the ability to tackle complex problems in bioinformatics and beyond.

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phishing-binary-classification

Aisuko Li · Hugging Face · 2024

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Binary Classification Datasets

Kounadis, Gerasimos · Zenodo (CERN European Organization for Nuclear Research) · 2024

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Binary classification results.

Muhammad Naveed Iqbal Qureshi (2921655), Beomjun Min (2921661), Hang Joon Jo (122179) et al. · Figshare · 2016

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ddG binary classification.

Lei Jia (303610), Ramya Yarlagadda (794457), Charles C. Reed (794458) · Figshare · 2015

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VK binary classification

Victoria Firsanova · Figshare · 2021

The dataset for binary text classification based on VK (russian social media) data.

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Optimal Binary Classification

Rachidi Kotchoni · 2025

It is shown that the Mean Integrated Square Error (MISE) of a binary classifier is a weighted average of its probabilities of type I (α) and type II errors (β). This provides a foundation for minimizing a linear cost function consisting of a weighted average of α and β to design an optimal classifier. Such a cost function is shown to have the interpretation of a MISE of the classifier under a subjective probability distribution. We derive the closed-form expression of the optimal α for the mean test, provide an equation that can be solved numerically to find the optimal cutoff of the Probit classifier, and illustrate the relevance of the results by simulation. In general, the optimal α for a significance test is different from the conventional 0.05 or 0.01 and the optimal cut-off for probabilistic classifiers deviates from 0.5.

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Robust Binary Classification

Resve Saleh, Sohaib Majzoub, A.K. Md. Ehsanes Saleh · 2025

This chapter addresses supervised machine learning algorithms for binary classification. The objective of binary classification is to establish a decision boundary that separates a given set of data into two classes based on their specified labels. The chapter begins with a detailed treatment of logistic regression, which is a well-known approach to binary classification. Then, a robust alternative based on the log-cosh loss function is presented. Other well-known approaches to binary classification are assessed for their robustness to outliers. Included are the support vector classifier (SVC), the support vector machine (SVM), k -nearest neighbors, and random forest. The primary goal of this chapter is to broaden the reader's understanding of the importance and advantages of robust methods in binary classification.

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Stretchy binary classification

Kar‐Ann Toh, Zhiping Lin, Лэй Сун et al. · Neural Networks · 2017

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