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.

scholarly journals PENERAPAN ALGORITMA C4.5 UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT (Studi Kasus : Koperia - Koperasi Warga Komplek Gandaria)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Teguh Budi Santoso ◽  
Dela Sekardiana
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Model ◽  
Decision Tree Classification ◽  
C4.5 Algorithm ◽  
Credit Worthiness ◽  
Loan Amount

<p><em>Current credit giving in KOPERIA (Koperasi Warga Komplek Gandaria) is still based on an objective process. Difficulties in determining the feasibility of giving credit are often experienced by cooperative managers, so that problems arise in the cooperative is a default payment of credit installments of customers in KOPERIA. This study aims to form a decision tree classification model to determine the customer's credit worthiness. In this study the application of C4.5 Algorithm, based on the Sets and Attributes used in this study, namely, the amount of income divided into 2 categories&gt; 5 million and 3-5 million, the amount of balance divided into three, namely&gt; 3 million, 1-3 million and &lt;1 Million, The Loan Amount is divided into three, namely 1-4 Months, 5-8 months, and 9-12 Months and Requirements with attributes of Business Capital, buying goods and others. In this study determine the appropriate root nodes, the classification results using C4.5 Algorithm shows that the accuracy of 97.5% is obtained, based on the results obtained shows that the c4.5 algorithm is suitable to be used to determine the feasibility of lending customers to KOPERIA.</em></p><p><strong><em>Keywords</em></strong><em>: Data Mining, C4.5 Algorithm</em><em>, loan feasibility</em></p>

Study of Financial Warning Ensemble Model for Listed Companies Based on Unbalanced Classification Perspective

2020 ◽  
Vol 16 (1) ◽  
pp. 32-48
Author(s):  
Wei Cong
Keyword(s):  
Decision Tree ◽  
Listed Companies ◽  
Hellinger Distance ◽  
Financial Data ◽  
Ensemble Classification ◽  
Classification Model ◽  
Base Classifier ◽  
C4.5 Decision Tree ◽  
Unbalanced Classification ◽  
Warning Model

Using the ensemble learning method to mine valuable information from a sea of financial data accumulated on the market of financial securities is very important for studying data processing. On the basis of financial data from A-share companies listed on Shanghai Stock Market, this article takes the perspective of unbalanced classification of ST stocks to carry out a study of the construction of a financial warning model for the listed companies. In our experiment, HDRF (HDRandom Forest, Hellinger Distance based Random Forest), ensemble classification models of Bagging, AdaBoost, and Rotation Forest, which take Hellinger distance decision tree (HDDT) as the base classifier, and the ensemble classification model which takes the C4.5 decision tree as the base classifier, are compared in respect of both the area under the ROC curve and the F-measure. As shown in the experimental results, the HDRF and the HDDT based classifier, as an ensemble method, are effective for financial data of listed companies.

scholarly journals Decision Tree Classification Model for Popularity Forecast of Chinese Colleges

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xiangxiang Zeng ◽  
Sisi Yuan ◽  
You Li ◽  
Quan Zou
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Classification Model ◽  
Prospective Students ◽  
Final Model ◽  
Decision Tree Classification ◽  
Tree Construction ◽  
Tree Models ◽  
Educational Field ◽  
Existing Data

Prospective students generally select their preferred college on the basis of popularity. Thus, this study uses survey data to build decision tree models for forecasting the popularity of a number of Chinese colleges in each district. We first extract a feature called “popularity change ratio” from existing data and then use a simplified but efficient algorithm based on “gain ratio” for decision tree construction. The final model is evaluated using common evaluation methods. This research is the first of its type in the educational field and represents a novel use of decision tree models with time series attributes for forecasting the popularity of Chinese colleges. Experimental analyses demonstrated encouraging results, proving the practical viability of the approach.

scholarly journals A Novel Impurity Measuring Technique for Decision Tree Learning in Machine Learning

2020 ◽  
Vol 9 (8) ◽  
pp. 506-512
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Big Data Analytics ◽  
Similarity Measures ◽  
Data Classification ◽  
Decision Tree Learning ◽  
Decision Tree Classification ◽  
C4.5 Decision Tree ◽  
Using Data ◽  
The Many

Decision tree classification is one of the most powerful data classification techniques in machine learning, data mining, big data analytics and split functionality is a crucial and inherently associated integral part of the decision tree learning. Many split similarity measures are proposed to determine the best split attribute and then partitioning the node data in decision tree learning accordingly. A new impurity measuring based split technique called (IMDT) for decision tree learning is proposed in this paper and it is used in obtaining experimental results. Many UCI machine learning dataset are employed in experimentation. The algorithm C4.5 is the most using data classification algorithm. The results obtained with the proposed approach are outperformed than the many existing decision tree classification algorithms in particular C4.5 decision tree algorithm.

scholarly journals PERANCANGAN APLIKASI PREDIKSI LAMA STUDI MAHASISWA BERDASARKAN MODEL KLASIFIKASI DATA LAMA STUDI MAHASISWA STMIK INDONESIA YANG TERBENTUK MENGGUNAKAN DECISION TREE

Petir ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. 96-103
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Decision Tree ◽  
Bachelor's Degree ◽  
Classification Model ◽  
Degree Programs ◽  
University Staff ◽  
Decision Tree Classification ◽  
Study Duration ◽  
Student’S Performance ◽  
The University ◽  
Predict Study

One way to improve quality in universities is through accreditation. One of the accreditation criteria is the student. Student’s performance must be monitored and evaluated. Regarding the study duration, the undergraduate bachelor’s degree programs typically takes four years to complete. It is important for the university staff to quickly identify which students are less likely to finish the degree on time. Therefore, it is necessary to predict the length of study for each student. The goal of this research is to predict study duration by building Decision Tree-based classifier model using NBTree algorithm. Then, an application is built by applying the classification model. Data used in this research are the grades and academic leave. Result shows that the Naïve Bayes Decision Tree classification model could predict study duration with the accuracy of 73.45%.

scholarly journals Handwritten digits recognition with decision tree classification: a machine learning approach

2019 ◽  
Vol 9 (5) ◽  
pp. 4446
Author(s):  
Tsehay Admassu Assegie ◽  
Pramod Sekharan Nair
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Learning Approach ◽  
Data Sets ◽  
The Past ◽  
Decision Tree Classification ◽  
Machine Learning Approach ◽  
Future Data ◽  
Class Labels

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

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