scholarly journals Optimasi Decision Tree menggunakan Particle Swarm Optimization untuk klasifikasi sel Pap Smear

2020 ◽  
Vol 7 (3) ◽  
pp. 572-579
Author(s):  
Toni Arifin
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Pap Smear ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Performance Report ◽  
Knowledge Rules ◽  
Feature Optimization

Penelitian ini menyajikan klasifikasi sel Pap Smear. Data yang digunakan adalah dataset Herlev yang berjumlah 917 data. Penelitian ini bertujuan untuk mengetahui apakah penggunaan algoritma optimasi particle swarm optimization dapat meningkatkan kinerja dari algoritma Decision tree dalam mengklasifikasikan data Sel Pap Smear. Tahapan dari penelitian ini adalah preprocessing, feature optimization, knowledge rules, evaluation dan performance report. Hasil dari penelitian ini menunjukkan bahwa algoritma Decision tree menghasilkan akurasi sebesar 91.39 % dengan AUC 0.858,  sedangkan penerapan algoritma particle swarm optimization pada Decision tree menghasilkan akurasi yang lebih baik yaitu sebesar 96.76 % dengan AUC 0.926.

scholarly journals Optimizing decision tree using particle swarm optimization to identify eye diseases based on texture analysis

2019 ◽  
Vol 8 (1) ◽  
pp. 59-63
Author(s):  
Toni Arifin ◽  
Asti Herliana
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Visual Impairment ◽  
Particle Swarm ◽  
Image Data ◽  
Swarm Optimization ◽  
Eye Disorders ◽  
Feature Optimization ◽  
Occurrence Matrix

The problem of visual impairment is a serious problem with increasing cases, ranging from visual impairment to the cause of blindness. This study examines the development of an identification application for the classification of patients with eye disorders using the Decision Tree (DT) method, which is optimized using Particle Swarm Optimization (PSO). This study used 311 eye image data, consisting of 233 normal eye images and 78 eye images with glaucoma, cataracts, and uveitis. The feature extraction used Gray Level Co-occurrence Matrix (GLCM), while the feature optimization used the PSO and the learning method used DT. This optimized visual impairment classification application can improve system accuracy to 88.09 %.

scholarly journals Design of median-type filters with an impulse noise detector using decision tree and particle swarm optimization for image restoration

2010 ◽  
Vol 7 (4) ◽  
pp. 859-882 ◽  
Author(s):  
Bae-Muu Chang ◽  
Hung-Hsu Tsai ◽  
Xuan-Ping Lin ◽  
Pao-Ta Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
High Performance ◽  
Impulse Noise ◽  
Median Filter ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Ordered Median ◽  
Mean Filter ◽  
Hybrid Detector

This paper proposes the median-type filters with an impulse noise detector using the decision tree and the particle swarm optimization, for the recovery of the corrupted gray-level images by impulse noises. It first utilizes an impulse noise detector to determine whether a pixel is corrupted or not. If yes, the filtering component in this method is triggered to filter it. Otherwise, the pixel is kept unchanged. In this work, the impulse noise detector is an adaptive hybrid detector which is constructed by integrating 10 impulse noise detectors based on the decision tree and the particle swarm optimization. Subsequently, the restoring process in this method respectively utilizes the median filter, the rank ordered mean filter, and the progressive noise-free ordered median filter to restore the corrupted pixel. Experimental results demonstrate that this method achieves high performance for detecting and restoring impulse noises, and outperforms the existing well-known methods.

scholarly journals A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1623 ◽  
Author(s):  
Huibing Zhang ◽  
Tong Li ◽  
Lihua Yin ◽  
Dingke Liu ◽  
Ya Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Decision Tree ◽  
Particle Swarm ◽  
Sensor Data ◽  
Gradient Boosting ◽  
Integrated Navigation ◽  
Swarm Optimization ◽  
Positioning Accuracy

The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression.

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