scholarly journals Pharmaceutical Data Search by Decision Trees

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Djamila Benhaddouche
Keyword(s):  
Pregnant Woman ◽  
Decision Trees ◽  
Data Base ◽  
Data Search ◽  
Case Classification

The number of information concerning the drugs that any professional of health must control in practice and the transformations which they undergo, make the regulation or the administration of drugs difficult for a pregnant woman. Techniques of excavation of data were developed to lead a model of classification of data according to precise criteria. One of   the most used of is the technique of the decision trees, a method making it possible to predict the membership of an individual to a class according to his characteristics; it is based primarily on the relevant attributes of the data base of the field to which it is applied. In our case classification of managed drugs or not with the pregnant woman will be done according to quarters of the pregnancy. The results of this technique will help the professionals of health to take a decision, to make a good regulation, to decrease the accidents related to the catch of inadequate drugs at the period of pregnancy with less risks for the child.

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.

Late Cenozoic tectono-sedimentary development of the Selendi and Uşak-Güre basins: a contribution to the discussion on the development of east–west and north trending basins in western Turkey

1997 ◽  
Vol 134 (2) ◽  
pp. 163-175 ◽  
Author(s):  
GÜROL SEYİTOĞLU
Keyword(s):  
Data Base ◽  
Early Miocene ◽  
Late Cenozoic ◽  
Western Turkey ◽  
The North ◽  
East West

This paper expands the K–Ar dating and palynologically controlled stratigraphical data base reported in earlier papers to the north trending Selendi and Uşak-Güre basins located to the north of east–west trending Alaşehir graben in western Turkey. These north trending basins began to form during Early Miocene times and most of their basin fills accumulated before 14 Ma, except for the youngest Asartepe formation. Recent studies of both east–west grabens and north trending basins show that they started to develop simultaneously during Early Miocene times under the north–south extensional regime, and the classification of the structures as ‘replacement’ and ‘revolutionary’ has no meaning for the Alaşehir graben and the basins located to its north.

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