The Speed and Accuracy Evaluation of Random Forest Performance by Selecting Features in the Transformation Data

AbstractIn predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF, which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases.

Download Full-text

Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir

Water ◽

10.3390/w12113231 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3231 ◽

Cited By ~ 1

Author(s):

Yijie Lu ◽

Zhen Zhang ◽

Danni Huang

Keyword(s):

Random Forest ◽

Landsat 8 ◽

Accuracy Evaluation ◽

Movement Velocity ◽

Topographic Features ◽

Alpine Regions ◽

Digital Elevation ◽

Ice Velocity ◽

Seasonal Snow Cover

Debris-covered glaciers are common features on the eastern Pamir and serve as important indicators of climate change promptly. However, mapping of debris-covered glaciers in alpine regions is still challenging due to many factors including the spectral similarity between debris and the adjacent bedrock, shadows cast from mountains and clouds, and seasonal snow cover. Considering that few studies have added movement velocity features when extracting glacier boundaries, we innovatively developed an automatic algorithm consisting of rule-based image segmentation and Random Forest to extract information about debris-covered glaciers with Landsat-8 OLI/TIRS data for spectral, texture and temperature features, multi-digital elevation models (DEMs) for elevation and topographic features, and the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) for movement velocity features, and accuracy evaluation was performed to determine the optimal feature combination extraction of debris-covered glaciers. The study found that the overall accuracy of extracting debris-covered glaciers using combined movement velocity features is 97.60%, and the Kappa coefficient is 0.9624, which is better than the extraction results using other schemes. The high classification accuracy obtained using our method overcomes most of the above-mentioned challenges and can detect debris-covered glaciers, illustrating that this method can be executed efficiently, which will further help water resources management.

Download Full-text

Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and SegNet

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021387 ◽

2021 ◽

Vol 18 (6) ◽

pp. 7790-7805

Author(s):

Fengcheng Zhu ◽

◽

Mengyuan Liu ◽

Feifei Wang ◽

Di Qiu ◽

...

Keyword(s):

Random Forest ◽

Regression Model ◽

Measurement Method ◽

Automatic Measurement ◽

Accuracy Evaluation ◽

Ultrasound Images ◽

Random Forest Regression ◽

Femur Length ◽

Low Contrast ◽

Noise Interference

<abstract> <p>The aim of this work is the preliminary clinical validation and accuracy evaluation of our automatic algorithms in assessing progression fetal femur length (FL) in ultrasound images. To compare the random forest regression model with the SegNet model from the two aspects of accuracy and robustness. In this study, we proposed a traditional machine learning method to detect the endpoints of FL based on a random forest regression model. Deep learning methods based on SegNet were proposed for the automatic measurement method of FL, which utilized skeletonization processing and improvement of the full convolution network. Then the automatic measurement results of the two methods were evaluated quantitatively and qualitatively with the results marked by doctors. 436 ultrasonic fetal femur images were evaluated by the two methods above. Compared the results of the above three methods with doctor's manual annotations, the automatic measurement method of femur length based on the random forest regression model was 1.23 ± 4.66 mm and the method based on SegNet was 0.46 ± 2.82 mm. The indicator for evaluating distance was significantly lower than the previous literature. Measurement method based SegNet performed better in the case of femoral end adhesion, low contrast, and noise interference similar to the shape of the femur. The segNet-based method achieves promising performance compared with the random forest regression model, which can improve the examination accuracy and robustness of the measurement of fetal femur length in ultrasound images.</p> </abstract>

Download Full-text

Speed and accuracy evaluation of additive manufacturing machines

Rapid Prototyping Journal ◽

10.1108/13552541111098644 ◽

2011 ◽

Vol 17 (1) ◽

pp. 64-75 ◽

Cited By ~ 66

Author(s):

Tomaz Brajlih ◽

Bogdan Valentan ◽

Joze Balic ◽

Igor Drstvensek

Keyword(s):

Additive Manufacturing ◽

Accuracy Evaluation ◽

Speed And Accuracy

Download Full-text

Comparison of Auditory, Language, Memory, and Attention Abilities in Children With and Without Listening Difficulties

American Journal of Audiology ◽

10.1044/2020_aja-20-00018 ◽

2020 ◽

Vol 29 (4) ◽

pp. 710-727

Author(s):

Beula M. Magimairaj ◽

Naveen K. Nagaraj ◽

Alexander V. Sergeev ◽

Natalie J. Benafield

Keyword(s):

Auditory Processing ◽

Short Term Memory ◽

Group Differences ◽

Long Term Memory ◽

Short Term ◽

Significant Group ◽

Term Memory ◽

Memory And Attention ◽

Speed And Accuracy

Objectives School-age children with and without parent-reported listening difficulties (LiD) were compared on auditory processing, language, memory, and attention abilities. The objective was to extend what is known so far in the literature about children with LiD by using multiple measures and selective novel measures across the above areas. Design Twenty-six children who were reported by their parents as having LiD and 26 age-matched typically developing children completed clinical tests of auditory processing and multiple measures of language, attention, and memory. All children had normal-range pure-tone hearing thresholds bilaterally. Group differences were examined. Results In addition to significantly poorer speech-perception-in-noise scores, children with LiD had reduced speed and accuracy of word retrieval from long-term memory, poorer short-term memory, sentence recall, and inferencing ability. Statistically significant group differences were of moderate effect size; however, standard test scores of children with LiD were not clinically poor. No statistically significant group differences were observed in attention, working memory capacity, vocabulary, and nonverbal IQ. Conclusions Mild signal-to-noise ratio loss, as reflected by the group mean of children with LiD, supported the children's functional listening problems. In addition, children's relative weakness in select areas of language performance, short-term memory, and long-term memory lexical retrieval speed and accuracy added to previous research on evidence-based areas that need to be evaluated in children with LiD who almost always have heterogenous profiles. Importantly, the functional difficulties faced by children with LiD in relation to their test results indicated, to some extent, that commonly used assessments may not be adequately capturing the children's listening challenges. Supplemental Material https://doi.org/10.23641/asha.12808607

Download Full-text

Dual Goals for Speed and Accuracy on the Same Performance Task

Journal of Personnel Psychology ◽

10.1027/1866-5888/a000063 ◽

2012 ◽

Vol 11 (3) ◽

pp. 118-126 ◽

Cited By ~ 3

Author(s):

Olive Emil Wetter ◽

Jürgen Wegge ◽

Klaus Jonas ◽

Klaus-Helmut Schmidt

Keyword(s):

Memory Scanning ◽

Performance Task ◽

Performance Tasks ◽

Trade Off ◽

Test Experiment ◽

Trade Offs ◽

New Finding ◽

Sternberg Paradigm ◽

Speed Accuracy ◽

Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

Download Full-text