decision tree
PulseAugur coverage of decision tree — every cluster mentioning decision tree across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New AI framework enhances wind farm power control with interpretable policies
Researchers have developed a novel policy distillation framework using explainable boosting machines (EBMs) to create an interpretable control policy for wind farms. This approach aims to improve the transparency of rei…
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Quantum-inspired data imputation shows improved distributional fidelity
Researchers have developed a novel quantum-inspired framework for data imputation, a crucial step in preprocessing clinical datasets. This method combines Principal Component Analysis (PCA) with quantum rotation-based r…
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Pairwise Difference Learning for Classification detailed in new arXiv papers · 3 sources tracked
Researchers have developed and analyzed Pairwise Difference Learning (PDL) for classification tasks, a meta-learning technique that predicts the difference between outcomes of two input instances rather than mapping ins…
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New 'ndscape' tool maps nested dichotomy structures for multiclass classification
Researchers have developed "ndscape," a novel two-dimensional mapping system designed to visualize the vast space of nested dichotomy structures used in multiclass classification problems. This embedding allows for the …
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New method quantifies robustness of Shapley values in tree-based models
Researchers have developed a new method using an imprecise Dirichlet model (IDM) to analyze the robustness of Shapley values in decision trees and random forests. This approach quantifies and examines interval-valued Sh…
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New method uses synthetic data for scalable meta-learning of interpretable decision trees
Researchers have developed a novel method for meta-learning interpretable decision tree models by synthetically generating large-scale, realistic datasets. This approach samples near-optimal decision trees, significantl…
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LLMs distilled into decision trees for better tabular classification · 2 sources tracked
Researchers have developed a new method to distill knowledge from Large Language Models (LLMs) into interpretable decision trees for tabular data classification. This approach addresses the limitations of directly using…
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Researchers Mine IPv6 Extension Header Patterns Using Explainable AI
Researchers have developed a method to mine explainable rules from IPv6 extension header (EH) patterns in network captures. The study utilized an interpretable temporal-logic rule miner on the JAMES dataset to identify …
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AI framework enhances legal reasoning with explainability for law enforcement
A new framework is proposed for decision support systems in law enforcement, focusing on explainability and compliance with regulations like the EU's General Data Protection Regulation (GDPR) and the Law Enforcement Dir…
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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…