scholarly journals Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio

2016 ◽  
Author(s):  
Yan Xia ◽  
Jian Shu ◽  
Na Xu ◽  
Hui Feng
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Classification Algorithm ◽  
Gain Ratio ◽  
Decision Tree Classification ◽  
Information Gain Ratio

An Improved SPRINT Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1685-1690 ◽  
Author(s):  
Zhi Kang Luo ◽  
Huai Ying Sun ◽  
De Wang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Learning Communities ◽  
Information Gain ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Gain Ratio ◽  
Information Gain Ratio ◽  
Improved Algorithm

This paper presents an improved SPRINT algorithm. The original SPRINT algorithm is a scalable and parallelizable decision tree algorithm, which is a popular algorithm in data mining and machine learning communities. To improve the algorithm's efficiency, we propose an improved algorithm. Firstly, we select the splitting attributes and obtain the best splitting attribute from them by computing the information gain ratio of each attribute. After that, we calculate the best splitting point of the best splitting attribute. Since it avoids a lot of calculations of other attributes, the improved algorithm can effectively reduce the computation.

scholarly journals PREDIKSI CALON MAHASISWA BARU MENGUNAKAN METODE KLASIFIKASI DECISION TREE

2015 ◽  
Vol 7 (1) ◽  
pp. 48
Author(s):  
Mambang Mambang ◽  
Finki Dona Marleny
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Internal Node ◽  
Terminal Node ◽  
Gain Ratio ◽  
Root Node ◽  
Information Gain Ratio

<p>Sebelum penyelengaraan pendidikan tenaga kesehatan memulai tahun ajaran baru, maka langkah awal akan dilaksanakan seleksi penerimaan mahasiswa baru yang berasal dari lulusan pendidikan menengah umum maupun kejuruan yang sederajat. Seleksi penerimaan mahasiswa baru ini bertujuan untuk menyaring calon mahasiswa dari berbagai latar belakang yang di sesuaikan dengan standar yang telah di tentukan oleh lembaga. Dalam penelitian ini bagaimana akurasi algoritma C4.5 untuk memprediksi kelulusan calon mahasiswa baru. Model decision tree merupakan metode prediksi klasifikasi untuk membuat sebuah tree yang terdiri dari root node, internal node dan terminal node. Berdasarkan hasil eksperimen dan evaluasi yang dilakukan maka dapat disimpulkan bahwa Algoritma C4.5 dengan Uncertainty didapatkan Akurasi 80,39%, Precision 94,44%, Recall 75,00% sedangkan dengan Algoritma C4.5 dengan Information Gain Ratio Akurasi 88,24%, Precision 98,28%, Recall 83,82%. </p>

An Improved ID3 Decision Tree Algorithm

2014 ◽  
Vol 962-965 ◽  
pp. 2842-2847 ◽  
Author(s):  
Xiao Juan Chen ◽  
Zhi Gang Zhang ◽  
Yue Tong
Keyword(s):  
Decision Tree ◽  
Information Entropy ◽  
Information Gain ◽  
Classification Algorithm ◽  
Learning Ability ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Decision Tree Classification ◽  
Id3 Algorithm ◽  
Improved Algorithm

As the classical algorithm of the decision tree classification algorithm, ID3 algorithm is famous for the merits of high classifying speed, strong learning ability and easy construction. But when used to make classification, the problem of inclining to choose attributions which have many values affect its practicality. This paper presents an improved algorithm based on the expectation information entropy and Association Function instead of the traditional information gain. In the improved algorithm, it modified the expectation information entropy with the improved Association Function and the number of the attributes values. The experiment result shows that the improved algorithm can get more reasonable and more effective rules.

scholarly journals Landslide susceptibility modelling based on decision tree classification model of machine learning: A case study of Kullu-Rohtang Pass transport corridor (Himachal Pradesh), India

2021 ◽  
Author(s):  
Nirbhav Sharma ◽  
Ram Babu Singh ◽  
Anand Malik ◽  
Maheshwar Sharma
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Landslide Susceptibility ◽  
Information Gain ◽  
Himachal Pradesh ◽  
Classification Model ◽  
Decision Tree Classification ◽  
Landslide Hazards ◽  
Susceptibility Model ◽  
Information Gain Ratio

Abstract Landslide hazards are responsible for causing substantial destruction and losses in mountainous region. In order to lessen the damage in these vulnerable areas, the key challenge is to predict the landslide events with accuracy and precision. The principal objective of the study conducted is to assess the landslide susceptibility along the transport corridor from Kullu to Rohtang Pass in Himachal Pradesh, India. To achieve this objective, a detailed landslide inventory has been prepared based on the imagery data and frequent field visits. A total of 197 landslides were taken under consideration including 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. Later, information gain ratio measure was used to identify the triggering factors having best score for eliminating the unimportant factors. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proved that the decision tree classification model of machine learning performed well and have a good accuracy in forecasting landslide susceptibility in the area considered for this study.

Uncertain data decision tree classification algorithm

2009 ◽  
Vol 29 (11) ◽  
pp. 3092-3095 ◽  
Author(s):  
Fang LI ◽  
Yi-yuan LI ◽  
Chong WANG
Keyword(s):  
Decision Tree ◽  
Uncertain Data ◽  
Classification Algorithm ◽  
Decision Tree Classification
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