scholarly journals Classification of the Placement Success in the Undergraduate Placement Examination According to Decision Trees with Bagging and Boosting Methods

2020 ◽  
Vol 41 (1) ◽  
pp. 93-105
Author(s):  
Tuğba TUĞ KAROĞLU ◽  
Hayrettin OKUT
Keyword(s):  
Decision Trees ◽  
Placement Success

A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

2008 ◽  
pp. 2978-2992
Author(s):  
Jianting Zhang ◽  
Wieguo Liu ◽  
Le Gruenwald
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Classification Accuracy ◽  
Decision Rules ◽  
Image Data ◽  
Remotely Sensed ◽  
Fast Speed ◽  
Remotely Sensed Image ◽  
Tree Approach

Decision trees (DT) has been widely used for training and classification of remotely sensed image data due to its capability to generate human interpretable decision rules and its relatively fast speed in training and classification. This chapter proposes a successive decision tree (SDT) approach where the samples in the ill-classified branches of a previous resulting decision tree are used to construct a successive decision tree. The decision trees are chained together through pointers and used for classification. SDT aims at constructing more interpretable decision trees while attempting to improve classification accuracies. The proposed approach is applied to two real remotely sensed image datasets for evaluations in terms of classification accuracy and interpretability of the resulting decision rules.

Setting Attribute Weights for Nearest Neighbor Learning Algorithms Using C4.5

1997 ◽  
Vol 11 (03) ◽  
pp. 405-415 ◽  
Author(s):  
Charles X. Ling ◽  
John J. Parry ◽  
Handong Wang
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Distance Function ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Simple Approach ◽  
Nearest Neighbour ◽  
Attribute Weights ◽  
New Methods

Nearest Neighbour (NN) learning algorithms utilize a distance function to determine the classification of testing examples. The attribute weights in the distance function should be set appropriately. We study situations where a simple approach of setting attribute weights using decision trees does not work well, and design three improvements. We test these new methods thoroughly using artificially generated datasets and datasets from the machine learning repository.

Solder joint defect classification based on ensemble learning

2017 ◽  
Vol 29 (3) ◽  
pp. 164-170 ◽  
Author(s):  
Hao Wu
Keyword(s):  
Random Forest ◽  
Solder Joint ◽  
Decision Trees ◽  
Printed Circuit Board ◽  
Solder Joints ◽  
High Accuracy ◽  
Ensemble Method ◽  
Circuit Board ◽  
Content Type

Purpose This paper aims to inspect the defects of solder joints of printed circuit board in real-time production line, simple computing and high accuracy are primary consideration factors for feature extraction and classification algorithm. Design/methodology/approach In this study, the author presents an ensemble method for the classification of solder joint defects. The new method is based on extracting the color and geometry features after solder image acquisition and using decision trees to guarantee the algorithm’s running executive efficiency. To improve algorithm accuracy, the author proposes an ensemble method of random forest which combined several trees for the classification of solder joints. Findings The proposed method has been tested using 280 samples of solder joints, including good and various defect types, for experiments. The results show that the proposed method has a high accuracy. Originality/value The author extracted the color and geometry features and used decision trees to guarantee the algorithm's running executive efficiency. To improve the algorithm accuracy, the author proposes using an ensemble method of random forest which combined several trees for the classification of solder joints. The results show that the proposed method has a high accuracy.

scholarly journals SEMANTIC INFORMATION EXTRACTION OF LANES BASED ON ONBOARD CAMERA VIDEOS

2018 ◽  
Vol XLII-3 ◽  
pp. 1643-1649
Author(s):  
L. Tang ◽  
T. Deng ◽  
C. Ren
Keyword(s):  
Information Extraction ◽  
Decision Trees ◽  
Hough Transform ◽  
Semantic Information ◽  
Automatic Detection ◽  
Autonomous Driving ◽  
Vanishing Point ◽  
Moving Vehicle ◽  
Video Images

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

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