decision tree
PulseAugur coverage of decision tree — every cluster mentioning decision tree across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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AI model predicts AML mutations from flow cytometry data
Researchers have developed an interpretable multi-instance learning model that can predict key molecular alterations in acute myeloid leukemia (AML) from routine flow cytometry data. This approach, which models patient …
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LLM-generated heart disease rules lag traditional models in accuracy
A new study published on arXiv evaluates the effectiveness of Large Language Models (LLMs) like GPT-4o and Claude Sonnet 4.6 in generating rules for heart disease prediction. The research found that traditional machine …
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Machine learning framework predicts restaurant food waste
Researchers have developed a machine learning framework to estimate daily food waste in restaurants, using operational data, weather, and event indicators. The study constructed a dataset of 77,980 records and employed …
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Machine learning models struggle with Canny edge detection for Parkinson's classification
A new study published on arXiv explores the effectiveness of machine learning models in classifying Parkinson's disease, with a particular focus on preprocessing techniques. Researchers found that while augmenting datas…
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New framework enhances explainability of Particle Swarm Optimization algorithms
Researchers have developed a new framework to improve the explainability of Particle Swarm Optimization (PSO) algorithms. This framework uses Exploratory Landscape Analysis (ELA) to characterize problem difficulty and e…
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AI models accurately classify Parkinson's disease severity using sensor data
Researchers have developed a machine learning approach to classify Parkinson's disease severity using data from triaxial inertial measurement unit (IMU) sensors. The study compared several classification models, with th…
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New framework enhances interpretable machine learning with heterogeneous experts
Researchers have developed a new framework for interpretable machine learning by extending the Mixture of Experts (MoE) model. This novel approach allows for heterogeneous experts, incorporating decision trees, linear s…
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Ensemble AI models achieve 99.52% accuracy in stroke prediction
Researchers have developed an ensemble of convolutional neural networks designed to improve the accuracy of stroke prediction. The study evaluated seven supervised machine learning algorithms, with ensemble methods like…
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Virgo detector uses AI pipeline to classify gravitational-wave glitches
Researchers have developed VIGILant, an automated pipeline to classify and visualize glitches in the Virgo gravitational-wave detector. The system employs both tree-based machine learning models and a ResNet34 convoluti…
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AI framework accurately identifies edible oils using Raman spectroscopy
Researchers have developed a novel approach using Raman spectroscopy and machine learning to authenticate edible oils, even within complex food matrices like fried potato chips. The study leverages Physics-Informed Arti…
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Building a Decision Tree Classifier From Scratch with NumPy
This article details the construction of a decision tree classifier from scratch using NumPy. It explains that decision trees operate by posing a series of questions about input features to narrow down possibilities and…
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AI and remote sensing track Dhaka's rapid urbanization and environmental shifts
A new study published on arXiv details the use of remote sensing and machine learning to analyze land use and vegetation changes in Dhaka District, Bangladesh, between 2019 and 2024. The research employed satellite imag…
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Deep Q-Network enhances cloud cyber defense with 99.72% accuracy
Researchers have developed a novel cybersecurity framework utilizing a Deep Q-Network (DQN) to enhance cloud infrastructure defense against sophisticated cyberattacks. This reinforcement learning-based approach trains a…
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Machine Learning vs. Deep Learning for Starbucks Review Sentiment Analysis
A new research paper compares the effectiveness of various machine learning and deep learning models for analyzing consumer sentiment in the retail coffee sector. The study focused on Starbucks reviews from ConsumerAffa…
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IoT Intrusion Detection: Beyond Accuracy to Explanation Cost and Stability
A new study published on arXiv evaluates machine learning models for Internet of Things (IoT) intrusion detection, focusing beyond just accuracy to include explanation cost, stability, and utility. Researchers construct…
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New method optimizes ensemble classifiers for faster predictions
Researchers have developed optimized sequential testing strategies for binary ensemble classifiers, such as random forests. These methods aim to reduce computational costs by evaluating base models sequentially and stop…
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GeoAI framework automates building footprint validation for GIS databases
Researchers have developed a GeoAI framework to automatically validate and purify building footprint data extracted from high-resolution imagery. This framework uses spatial feature engineering and machine learning clas…
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ArborEnum algorithm enumerates decision tree Rashomon sets over continuous features
Researchers have developed a new algorithm called ArborEnum that can enumerate decision tree Rashomon sets over continuous features. This algorithm addresses the limitations of previous methods that required binarizing …
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New method simplifies decision trees for Markov decision processes
Researchers have developed a new method called dtControl2+$\\varepsilon$ to create smaller, more understandable decision trees for controllers in Markov decision processes. This approach allows for tunable simplificatio…
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New dtControl2+$\\varepsilon$ method simplifies decision trees for Markov decision processes
Researchers have developed a new method called dtControl2+$\varepsilon$ to create smaller, more explainable decision trees for Markov decision processes. This technique allows for tunable simplification of controllers b…