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The study of cross-validated bagging fuzzy-id3 algorithm for breast cancer classification

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 2567-2577页

作者： idris, Nur Farahaina Ismail, Mohd Arfian Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pekan Pahang Malaysia

Globally, the second most common cause of death for female cancer patients is breast cancer. In the United States, about 11,000 females aged below 40 are diagnosed with invasive breast cancer each year. Early detection of breast cancer is the foundation for preventing the progression of the disease, and the diagnosis can be conducted using intelligent systems for quicker detection. Based on the FUZZYDBD method and bootstrap aggregation (bagging) technique, the Bagging fuzzy-id3 algorithm (BFid3) was proposed for this study. This method combined the techniques of the fuzzy system, id3 algorithm and bagging. For BFid3's data fuzzification, the automatic fuzzy database definition method, known as the FUZZYDBD method, would assist in developing the fuzzy database. One of the weaknesses of the id3 algorithm is its incapability to handle continuous data. The problem was resolved via the linguistic variable replacement and data fuzzification in the BFid3. Meanwhile, this paper's implementation of the bagging technique improved the generalization ability and reduced overfitting. Additionally, BFid3 was verified through an extensive comparison with several existing methods to investigate the competency of the proposed method. The study identified that BFid3 was proficient in breast cancer classification.

关键词： Fuzzy system id3 algorithm bagging breast cancer

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Diagnosis of Hepatitis C Virus Based on id3 algorithm

Diagnosis of Hepatitis C Virus Based on ID3 Algorithm

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作者： Yue Che Dalian Maritime University

Infection with the hepatitis C virus(HCV) is what causes hepatitis *** has one of the highest hepatitis C infection rates in the world(13%-15%),and this illness affects more than 170 million people *** a result,there is an enormous need for the diagnosis of this *** recent years,researchers have made significant progress in this field with different machine learning algorithms and have had the ability to make a relatively precise ***,within the machine learning algorithms that the researchers used,decision trees,particularly the id3(Iterative Dichotomiser 3) algorithm,have been *** paper uses this algorithm in the diagnosis of hepatitis C,aiming to test the accuracy and explore the possibility of introducing this approach into application in ***,the novel method of applying the id3 algorithm in the diagnosis of hepatitis C can produce relatively accurate results compared with those traditional approaches,demonstrating that this method can be used in practice and is a powerful approach for diagnosing people who have the hepatitis C virus.

关键词： Hepatitis C Machine learning id3 algorithm Decision Tree

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Breast cancer disease classification using fuzzy-id3 algorithm with FUZZYDBD method: automatic fuzzy database definition

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PEERJ COMPUTER SCIENCE 2021年 7卷 e427页

作者： idris, Nur Farahaina Ismail, Mohd Arfian Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pekan Pahang Malaysia

Breast cancer becomes the second major cause of death among women cancer patients worldwide. Based on research conducted in 2019, there are approximately 250,000 women across the United States diagnosed with invasive breast cancer each year. The prevention of breast cancer remains a challenge in the current world as the growth of breast cancer cells is a multistep process that involves multiple cell types. Early diagnosis and detection of breast cancer are among the greatest approaches to preventing cancer from spreading and increasing the survival rate. For more accurate and fast detection of breast cancer disease, automatic diagnostic methods are applied to conduct the breast cancer diagnosis. This paper proposed the fuzzy-id3 (Fid3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. This study aims to resolve the limitation of an existing method, id3 algorithm that unable to classify the continuous-valued data and increase the classification accuracy of the decision tree. Fid3 algorithm combined the fuzzy system and decision tree techniques with id3 algorithm as the decision tree learning. FUZZYDBD method, an automatic fuzzy database definition method, would be used to design the fuzzy database for fuzzification of data in the Fid3 algorithm. It was used to generate a predefined fuzzy database before the generation of the fuzzy rule base. The fuzzified dataset was applied in Fid3 algorithm, which is the fuzzy version of the id3 algorithm. The inference system of Fid3 algorithm is simple with direct extraction of rules from generated tree to determine the classes for the new input instances. This study also analysed the results using three breast cancer datasets: WBCD (Original), WDBC (Diagnostic) and Coimbra. Furthermore, the comparison of Fid3 algorithm with the existing methods is conducted to verify the proposed method's capability and performance. This study identified that the combination of Fid3 algorithm with FUZZYDBD

关键词： Fuzzy id3 algorithm Fid3 algorithm Fuzzy decision tree FUZZYDBD Breast cancer Classification Fuzzification

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An improved improved id3 algorithm for medical data classification

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 65卷 474-487页

作者： Yang, Shuo Guo, Jing-Zhi Jin, Jun-Wei Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China

Data mining techniques play an important role in clinical decision making, which provides physicians with accurate, reliable and quick predictions through building different models. This paper presents an improved classification approach for the prediction of diseases based on the classical Iterative Dichotomiser 3 (id3) algorithm. The improved id3 algorithm overcomes multi-value bias problem when selecting test/split attributes, solves the issue of numeric attribute discretization and stores the classifier model in the form of rules by using a heuristic strategy for easy understanding and memory savings. Experiment results show that the improved id3 algorithm is superior to the current four classification algorithms (J48, Decision Stump, Random Tree and classical id3) in terms of accuracy, stability and minor error rate. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： id3 algorithm Decision tree Balance function Numeric attribute discretization Rule representation of classifier model

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An Improved id3 classification algorithm for solving the backbone of proposition formulae 18

An Improved ID3 classification algorithm for solving the bac...

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18th IEEE International Conference on Pervasive Intelligent and Computing (PICom)

作者： Liang, Tian Wang, Xiaofeng Wang, Shuai Wang, Xingkai North Minzu Univ Dept Comp Sci & Engn Yinchuan 750021 Ningxia Peoples R China

ISBN: (纸本)9781728166094

Backbone is a set of special variables of propositional formula, while it had important applications in 3-sat problems and SAT-based applications, so it is very important to solve the backbone efficiently. This paper proposed id3_backbone algorithm for computing backbones of proposition formulae, this is because the principle of id3 algorithm is to calculate the attribute information gain to determine the decision attribute, which can reduce the number of calls to the SAT solver. Experiments showed: id3_Backbone algorithm can guarantee 75% accuracy under the condition of improving the efficiency, it propose an effective method to solve the problem of backbone set.

关键词： backbone boolean formulae id3 algorithm

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Spam Email Detection using id3 algorithm and Hidden Markov Model

Spam Email Detection using ID3 Algorithm and Hidden Markov M...

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Conference on Information and Communication Technology (CICT)

作者： Kumar, Vikrant Monika Kumar, Parveen Sharma, Ambalika Natl Inst Technol Srinagar Uttarakhand India Indian Inst Technol Roorkee Roorkee Uttar Pradesh India

ISBN: (纸本)9781538682159

Emails are the way to communicate over the Internet but this method of communication is bothersome by the Spam emails. Spam emails are the waste of memory, money, time and communication bandwidth. Thus, Spam emails needed to be identified and culminated. Hence, use of the id3 algorithm for making the decision trees and the Hidden Markov Model for calculating the probabilities of the events that may occur is used in this paper as a combination to identify the emails as Spam or ham. The model labels the emails as Spam or ham by calculating total probability of an email using all posteriorly classified words in emails and then supervising all processed emails by making their decision trees. For this purpose, an Enron dataset of 5172 emails is used that contains 2086 Spam and 2086 ham pre-classified emails. The experimental result on the given dataset shows that an accuracy of 89% is obtained on the Spam emails.

关键词： id3 algorithm Case-folding Stemming Lemmatization Hidden Markov Model

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Improvement of id3 algorithm Based on Simplified Information Entropy and Coordination Degree

Improvement of ID3 Algorithm Based on Simplified Information...

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Chinese Automation Congress (CAC)

作者： Wang Ying-ying Li Yi-bin Rong Xue-wen Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Shandong Univ Jinan Shandong Peoples R China

ISBN: (纸本)9781538635247

In data classification mining, the decision tree method is a key algorithm. id3 (Iterative Dichotomiser 3) algorithm which was presented by Quinlan is a famous decision tree algorithms, but id3 has some shortcomings such as high complex computation in computing the information entropy expression, multivalue bios problem in the process of selecting an optimal attribute, large scales, etc. In order to solve the above problems, the improved id3 algorithm is proposed, which combines the simplified information entropy with coordination degree in rough set theory. The experiment result has proved the feasibility of the optimized way.

关键词： decision tree algorithm id3 algorithm information entropy coordination degree

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The Analysis and Optimization of Decision Tree Based on id3 algorithm 9th

The Analysis and Optimization of Decision Tree Based on ID3 ...

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9th International Conference on Modelling, identification and Control (ICMIC)

作者： Zhang, He Zhou, Runjing Inner Mongolia Univ Coll Elect Informat Hohhot Peoples R China

ISBN: (纸本)9781509065738

Decision tree is one of the complex algorithms in the fields of pattern recognition and data mining, which is not only related to database, artificial intelligence and other disciplines, but has great theoretical research value. This paper used standard data set as original discrete experimental data, and the entropy and information gain of each attribute of the data was calculated to implement the classification of data. After traversing the structure of tree, the attributes corresponding to the information gain that reduces the maximum entropy were selected as optimal classification attribute for the generation of the decision tree. The simulation results show that the classification accuracy of id3 decision tree was 6-7 percentage points higher than the other classification algorithms in this paper, meanwhile, the generation of the tree structure was apparently complex. In addition, this paper achieved an optimized structure of the decision tree, which is simplified to 5 node structure from the initial 19 nodes for improving the efficiency of the algorithm on the premise of ensuring low error rate which was at the same level as other classification algorithms.

关键词： id3 algorithm Decision Tree Structural Optimization Data Mining Pattern Recognition

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Improving id3 algorithm by Using A* Search 21

Improving ID3 Algorithm by Using A* Search

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21st International Computer Science and Engineering Conference (ICSEC)

作者： Kaewrod, Nicha Jearanaitanakij, Kietikul King Mongkuts Inst Technol Ladkrabang Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781538607879

id3 is one of the most widely used algorithms for creating a classification decision tree. However, the traditional id3 algorithm has a difficulty when there are equally important attributes during the decision node construction. It randomly selects one of the most important attributes to serve as the current decision node. This behavior may lead to the resulting decision tree which contains unnecessary depths and suboptimal accuracy. The purpose of this paper is to find the near optimal depth decision tree of the dataset, which contains an equally important attributes problem, by using the A-Star (A*) search. The experiment results on four standard datasets from UCI indicate that the proposed algorithm can significantly reduce the decision tree's depth and still maintain the classification accuracy.

关键词： Decision tree id3 algorithm A* search Classification

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Improving id3 algorithm by Combining Values from Equally Important Attributes 21

Improving ID3 Algorithm by Combining Values from Equally Imp...

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21st International Computer Science and Engineering Conference (ICSEC)

作者： Kraidech, Suratchanan Jearanaitanakij, Kietikul King Mongkuts Inst Technol Ladkrabang Dept Comp Engn Fac Engn Bangkok Thailand

ISBN: (纸本)9781538607879

id3 is a well-known algorithm which is used in the classification task of the decision tree learning. Although a lot of research provides improvements on the traditional id3 algorithm with various strategies, no attempt was made on id3 to address the problem when there are more than one attribute that can be placed at a particular node, i. e. those attributes are equally important. This paper proposes a new variation of id3 to combine equally important attributes into a single node of the decision tree classification. The Connect-4 dataset from UCI is used in our experiment since the dataset contains many attributes and instances which can easily encounter the equally important attribute problem. The experimental results show that our proposed method significantly reduces the average depth of the decision tree generated by id3 algorithm while the average accuracy rate is still preserved.

关键词： Decision tree id3 algorithm Classification Attribute combining

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