The classification algorithm flow chart for binary classification among NC, EMCI, LMCI and AD groups. by Qian Zhang (49333)

Comparison of NQE combined with SVC, NN, and QCNN classifiers. The table shows the number of test cases, comparison with classical classifiers, and data imbalance issues in this an... by Aninda Astuti (21736334)

For each classification algorithm, the classification accuracies for binary subject groups NC versus EMCI, NC versus LEMC, EMCI versus LEMC, EMCI versus AD, LMCI versus AD, AD vers... by Qian Zhang (49333)

Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data by Seokho Lee (10088)

“…<p>We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. …”

DataSheet_1_Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.pdf by Marcel Dahms (9160118)

“…Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.…”

Binary Database of Patagonian Archaeological Technology from Hunter–gatherer mobility and technological landscapes in southernmost South America: a statistical learning approach. by Ivan Briz i Godino (719098)

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

“…To address this, the research integrates a modified A* pathfinding algorithm with a U-Net convolutional neural network, a custom statistical binary classification method, and a personalized Min-Max connectivity threshold to automate the detection of astrocyte connectivity.…”

EvoImp’s GA structure example. by Antonio Fernando Lavareda Jacob Junior (17816730)

Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

Data_Sheet_1_Improving Crowdsourcing-Based Image Classification Through Expanded Input Elicitation and Machine Learning.PDF by Romena Yasmin (12970919)

“…Five types of input elicitation methods are tested: binary classification (positive or negative); the (x, y)-coordinate of the position participants believe a target object is located; level of confidence in binary response (on a scale from 0 to 100%); what participants believe the majority of the other participants' binary classification is; and participant's perceived difficulty level of the task (on a discrete scale). …”

Neyman-Pearson Multi-Class Classification via Cost-Sensitive Learning by Ye Tian (220278)

“…Simulations and real data studies validate the effectiveness of our algorithms. …”

Melanoma Detection by Means of Multiple Instance Learning by Annabella Astorino (11644324)

“…In this work we have applied a MIL algorithm on some clinical data constituted by color dermoscopic images, with the aim to discriminate between melanomas (positive images) and common nevi (negative images). …”

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

“…Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. …”

Demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies by Antonis Michanikou (8996667)

“…The Variables included in the first three excel tabs were the following: Participant ID (Unique serial number for patient participating in the study), % Time Before (Percentage of time with data before protocol implementation), % Time After (Percentage of time with data after protocol implementation), Timestamp (Date and time of event occurrence), Indoor/Outdoor (Categorical- Classification of GPS signals to Indoor and Outdoor and null(missing value) based on distance from participant home), Filling algorithm (Imputation algorithm), SSID (Wireless network name connected to the smartwatch), Wi-Fi Signal Strength (Connection strength via Wi-Fi between smartwatch and home’s wireless network. (0 maximum strength), IMEI (International mobile equipment identity. …”

Schematic overview of SINATRA Pro: A novel framework for discovering biophysical signatures that differentiate classes of proteins. by Wai Shing Tang (9541179)

DataSheet_1_Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer.docx by Shima Sepehri (11574997)