IEEE

Data-Centric Deep Learning for Monkeypox Detection: Hyperdatasets Toward Robust and Generalizable Medical AI

Abstract: In this work, a structured hierarchy of hyperdatasets for Monkeypox image classification is presented, aiming to evaluate the robustness and generalization ability of deep classification ...
marktechpost

How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost

Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over the years, models ...
IEEE

Research on the Application of Binary Classification Dataset Based on Integrated Learning Model

Abstract: The binary classification problem is a fundamental and core problem type in machine learning, and many machine learning algorithms, such as logistic regression and tree models, are widely ...
marktechpost

Focal Loss vs Binary Cross-Entropy: A Practical Guide for Imbalanced Classification

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both ...
Visual Studio Magazine

Tsetlin Machine Binary Classification Using C#

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
eLife

Fast evolutionary turnover and overlapping variances of sex-biased gene expression patterns defy a simple binary sex classification of somatic tissues

This study presents data on sex differences in gene expression across organs of four mice taxa. The authors have generated a unique and convincing dataset that fills a gap left by previous studies.
Scientific Research Publishing

Saito, T. and Rehmsmeier, M. (2015) The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets.

ABSTRACT: Accurately predicting individual responses to antidepressant treatment is a critical step toward achieving personalized psychiatry and minimizing the ...
Visual Studio Magazine

AdaBoost Binary Classification Using C#

Dr. James McCaffrey from Microsoft Research presents a C# program that illustrates using the AdaBoost algorithm to perform binary classification for spam detection. Compared to other classification ...
Scientific Research Publishing

Kirasich, K., Smith, T. and Sadler, B. (2018) Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets. SMU Data Science Review, 1, Article 9.

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...
Frontiers

A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA)

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely ...