Glaucoma Classification Model Based on GDx VCC Measured Parameters by Decision Tree

2009 ◽  
Vol 34 (6) ◽  
pp. 1141-1147 ◽  
Author(s):  
Mei-Ling Huang ◽  
Hsin-Yi Chen
Keyword(s):  
Decision Tree ◽  
Classification Model ◽  
Model Based ◽  
Gdx Vcc

scholarly journals PENERAPAN PARTICLE SWARM OPTIMIZATION PADA ALGORITMA C 4.5 UNTUK SELEKSI PENERIMAAN KARYAWAN

2018 ◽  
Vol 4 (2) ◽  
pp. 51-59
Author(s):  
Agus Wiyatno
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Classification Tree ◽  
Particle Swarm ◽  
Classification Model ◽  
Accurate Information ◽  
Swarm Optimization ◽  
Organization Model ◽  
Goods And Services ◽  
Model Based

The Employees are the most vital element of the company as they had a big contribution and involved almost for all section on how the company will go up and down. Employees and the company affect the efficiency, effectiveness,designing, producing goods and services, oversee the quality, market products, allocating financial resources, and determines the overall goals and strategies of the organization. Therefore, organizations need accurate information and sustainable in order to get suitable candidates with the qualifications of the organization. Model algorithms are widely used in research related to the employee is C4.5 decision tree classification model. Advantages of using adecision tree classification models are the result of the decision tree is simple and easy to understand. Many studies using the method of decision tree and classification tree in predicting the employees selection but results theaccuracy of the resulting value is less accurate. In this study created a C 4.5 Algorithm model and C 4.5 Algorithm model based on particle swarm optimization to get the rule in employees selection and provide a more accuratevalue of accuracy. After testing C 4.5 algorithm model based on particle swarm optimization, Implementation of particle swarm optimization can produce accuracy value of C 4.5 algorithm model from 80.80 % to 85.20 % and theAUC value from 0.878 to 0.891. By the formation the model selection of employees, the company can be helped for employee selection.

scholarly journals Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 526
Author(s):  
Yang Han ◽  
Chunbao Liu ◽  
Lingyun Yan ◽  
Lei Ren
Keyword(s):  
Decision Tree ◽  
Time Window ◽  
Threshold Value ◽  
Recognition System ◽  
Tree Structure ◽  
Classification Model ◽  
Measurement Unit ◽  
Local Optima ◽  
Mode Recognition ◽  
Locomotion Mode

Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.

A DECISION TREE-BASED CLASSIFICATION APPROACH TO RULE EXTRACTION FOR SECURITY ANALYSIS

2006 ◽  
Vol 05 (01) ◽  
pp. 227-240 ◽  
Author(s):  
N. REN ◽  
M. ZARGHAM ◽  
S. RAHIMI
Keyword(s):  
Decision Tree ◽  
High Performance ◽  
Security Analysis ◽  
Predictive Performance ◽  
Classification Model ◽  
Stock Selection ◽  
Selection Rules ◽  
Stock Portfolios ◽  
Decision Tree Classification ◽  
C4.5 Decision Tree

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.

Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

2014 ◽  
Vol 2 (4) ◽  
pp. 63-70 ◽  
Author(s):  
Danyel Jennen ◽  
Jan Polman ◽  
Mark Bessem ◽  
Maarten Coonen ◽  
Joost van Delft ◽  
...  
Keyword(s):  
Liver Injury ◽  
Clinical Chemistry ◽  
Classification Model ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Chemistry Data ◽  
Model Based ◽  
Injury Classification

The new chronic lithiasic disease management and risk classification model, based on a Kaiser Permanente Pyramid (KPP) design. The 3-year results

2021 ◽  
Vol 79 ◽  
pp. S346-S347
Author(s):  
F. Gómez Palomo ◽  
D.G. Ordaz Jurado ◽  
A. Budía Alba ◽  
D. Vivas-Consuelo ◽  
P. Bahilo Mateu ◽  
...  
Keyword(s):  
Disease Management ◽  
Risk Classification ◽  
Classification Model ◽  
Kaiser Permanente ◽  
Model Based

scholarly journals Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2910
Author(s):  
Kei Suzuki ◽  
Tipporn Laohakangvalvit ◽  
Ryota Matsubara ◽  
Midori Sugaya
Keyword(s):  
Feature Selection ◽  
Single Channel ◽  
Feature Selection Method ◽  
Classification Model ◽  
Features Selection ◽  
Selection Methods ◽  
Emotion Classification ◽  
Model Based ◽  
Physiological Indexes ◽  
Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

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