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43 results for “decision trees”
Datasets used in the papers: STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines & ODTE - An ensemble of multi-class SVM-based oblique decision trees
<p>These are the 49 datasets used in the benchmark. 45 of them are from the UCI machine learning repository, while the other 4 correspond to a problem about fecundity estimation for fisheries</p>
Development of Risk Score Model and Decision Tree Algorithm for Predicting Infections With CRKp in Colonized Patients
ClinicalTrials.gov study NCT04229511. IPD Sharing: NO. Countries: 1. Publications: 7.
Validity and Inter-rater Reliability for Early Recognition of Spasticity Using the Decision Tree Tool
ClinicalTrials.gov study NCT06381999. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Learning to see the wood for the trees: machine learning, decision trees and the classification of isolated theropod teeth
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Data from: Foraging decisions with conservation consequences: Interaction between beavers and invasive tree species
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Satellite-derived long-term estimates of full-coverage PM1 concentrations across China based on a stacking decision tree model
<p>The open data uploaded by Rui Li</p>
Data from: Give the machine a hand: a Boolean time-based decision-tree template for rapidly finding animal behaviours in multi-sensor data
1. The development of multi-sensor animal-attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. 2. The high volumes of data, are pushing us towards machine-learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more likely to become processor limited and thereby take appreciable amounts of time to resolve behaviours. 3. We suggest a Boolean approach whereby critical changes in recorded parameters are used as sequential templates with defined flexibility (in both time and degree) to determine individual behavioural elements within a behavioural sequence that, together, makes up a single, defined behaviour. 4. We tested this approach, and compared it to a suite of other behavioural identification methods, on a number of behaviours from tag-equipped animals; sheep grazing, penguins walking, cheetah stalking prey and condors thermalling. 5. Overall behaviour recognition using our new approach was better than most other methods due to; (i) its ability to deal with behavioural variation and (ii) the speed with which the task was completed because extraneous data are avoided in the process. 6. We suggest that this approach is a promising way forward in an increasingly data-rich environment and that workers sharing algorithms can provide a powerful library for the benefit of all involved in such work.
Development of Decision Trees for the Selection of Pavement Maintenance and Rehabilitation Activities in South-Central United States
<p>Over time, new pavements deteriorate under the combined effects of traffic loading and the environment, no matter how well-designed or constructed. In general, maintenance and rehabilitation activities are employed to slow down or reset the rate of pavement deterioration. Cement-Stabilized Full Depth Reclamation (CSFDR) is a common rehabilitation treatment used by transportation agencies, specifically in Louisiana. Likewise, Ultra-Thin overlay (UTO) is a pavement maintenance treatment that has increased in popularity in recent years in Region 6. Yet, several gaps exist in the literature regarding the long-term field performance and cost-effectiveness of these two treatments especially in hot and humid climates. Therefore, the key objectives of this study were to assess the immediate benefits and long-term field performance as well as the cost-effectiveness of these two treatments in Louisiana. To achieve these objectives, numerous CSFDR and UTO projects were identified from the Louisiana Department of Transportation (LaDOTD) Pavement Management System (PMS) database and analyzed in terms of alligator cracks, rutting, random cracks, and roughness over a monitoring period of up to 15 years. Results indicated that the performance of CSFDR is significantly affected by the pre-treatment pavement conditions, applied overlay thickness, and traffic. Results also indicated that CSFDR projects would usually fail due to the development of random cracks. This could be attributed to the development of shrinkage cracks, which is a common problem with cement stabilization in Louisiana. A regression model was developed to predict the service life of CSFDR based on project conditions. Results also showed that UTO considerably extended the Pavement Service Life (PSL) for all the distress indices. This extension varied based on the pre-treatment pavement conditions and traffic level. As such, a predictive model was developed, with reasonable accuracy, to predict the extension in PSL of UTO based on project conditions. The developed models in this project for CSFDR and UTO will help state agencies make effective decisions for the maintenance and rehabilitation of their pavements.</p>
SAT-based Decision Tree Learning for Large Data Sets - Results and Source code
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Data from: Give the machine a hand: a Boolean time-based decision-tree template for rapidly finding animal behaviours in multi-sensor data
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Data from: Using decision trees to understand structure in missing data
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Raw Data Training Decision Tree Rice Disease
<p>this data is data set for training decision tree in research rice plant disease.</p>
Research on the Application of Decision Tree in Child Life
ClinicalTrials.gov study NCT07325890. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Decision Tree Analysis of Hemodialysis Arteriovenous Access Choices and Creations - a Multiple-center Retrospective Cohort Study
ClinicalTrials.gov study NCT06364449. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Skin Autofluorescence (AF) Decision Tree in Detecting Impaired Glucose Tolerance (IGT) or Diabetes Mellitus (DM)
ClinicalTrials.gov study NCT01406665. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Empirical Mode Decomposition and Decision Tree in Sarcopenia
ClinicalTrials.gov study NCT05396404. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using Decision Trees to Detect and Isolate Leaks in the J-2X
**Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine** Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center **Abstract** The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. **Introduction** The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool [http://www.gensym.com](http://www.gensym.com) to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro
Multi-center Clinical Study on the Decision Tree of Precision Hepatectomy in China Precision Hepatectomy Decision Tree
ClinicalTrials.gov study NCT05986383. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Distributed Decision-Tree Induction in Peer-to-Peer Systems
This paper offers a scalable and robust distributed algorithm for decision-tree induction in large peer-to-peer (P2P) environments. Computing a decision tree in such large distributed systems using standard centralized algorithms can be very communication-expensive and impractical because of the synchronization requirements. The problem becomes even more challenging in the distributed stream monitoring scenario where the decision tree needs to be updated in response to changes in the data distribution. This paper presents an alternate solution that works in a completely asynchronous manner in distributed environments and offers low communication overhead, a necessity for scalability. It also seamlessly handles changes in data and peer failures. The paper presents extensive experimental results to corroborate the theoretical claims.
ARC Code TI: IND: Creation and Manipulation of Decision Trees from Data
IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or omitted. One of the attributes is delegated the 'target' and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.