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6th International Conference on Machine Learning and Cybernetics

作者： Li, Guo-Gang Li, Yan Li, Fa-Chao Jin, Chen-Xia Hebei Univ Sci & Technol Coll Sci Shijiazhuang 050018 Peoples R China Hebei Univ Sci & Technol Coll Econ & Mgmt Shijiazhuang 050018 Peoples R China

ISBN: (纸本)9781424409723

When noise exists in case base, high quality knowledge is hard to obtain by id3 algorithm. For the weakness, by introducing the concept of second learning, the noisy data can be removed, which not only develop the decision tree, but also it can make good structure tree generate. So that we can abstract good rules information, and make the desirable tree more accurate. Especially, the more the data can be mined by decision tree algorithm, the better the efficiency and performance of the algorithm is, and the more obvious the superiority of algorithm is. This paper states the basic idea of algorithm implementation process, performance analysis and accuracy proof in detail.

关键词： decision tree id3 algorithm noise second learning accuracy rate

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Application of Decision Tree Intelligent algorithm in Mathematical Modeling Structure of Automation Control 1

Application of Decision Tree Intelligent Algorithm in Mathem...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mou, Jiazheng Jiangsu Jari Information Technology Co. Jiangsu Lianyungang China

ISBN: (纸本)9798350316469

In order to establish a delay automatic control model with lower control error and better reduction of uplink load, based on existing work, this paper adopted decision tree intelligent algorithm to mathematically model the automatic control. Based on this, this article studied the collaborative scheduling mechanism of decision tree intelligent algorithms and the collaborative scheduling of resources. Firstly, this article analyzed the delay uncertainty in the communication process and adopted a probability graph based method to simplify the probability model composed of delay uncertainty, thereby improving the efficiency of delay automatic control. Next, this article explored an online learning method based on decision tree intelligent algorithms to simplify probability models and model them mathematically. Finally, the communication characteristics of the decision tree intelligent algorithm were simulated and control experiments were conducted on the proposed scheme. The experimental results showed that the CART algorithm achieved the best classification results on the four evaluation indicators of F-Measure, G-Mean, AUROC (Area Under the Receiver Operating Characteristic), and AUPRC (Area Under the Precision Recall Curve) of the diagnostic model for the test dataset established using different modeling methods, with values of 0.92, 0.894, 0.907, and 0.9, respectively. © 2023 IEEE.

关键词： Decision Tree algorithm id3 algorithm Information Increment Mathematical Modeling

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Data Mining based Fragmentation and Prediction of Medical Data

Data Mining based Fragmentation and Prediction of Medical Da...

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： Hnin Wint Khaing University of Computer Studies Mandalay Myanmar

ISBN: (纸本)9781612848396

Data mining concerns theories,methodologies,and in particular,computer systems for knowledge extraction or mining from large amounts of *** rule mining is a general purpose rule discovery *** has been widely used for discovering rules in medical *** diagnosis of diseases is a significant and tedious task in *** detection of heart disease from various factors or symptoms is an issue which is not free from false presumptions often accompanied by unpredictable *** the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable *** this paper,we presented an efficient approach for the prediction of heart attack risk levels from the heart disease ***, the heart disease database is clustered using the K-means clustering algorithm,which will extract the data relevant to heart attack from the *** approach allows mastering the number of fragments through its k parameter. Subsequently the frequent patterns are mined from the extracted data,relevant to heart disease,using the MAFIA （Maximal Frequent Itemset algorithm）*** machine learning algorithm is trained with the selected significant patterns for the effective prediction of heart attack. We have employed the id3 algorithm as the training algorithm to show level of heart attack with the decision *** results showed that the designed prediction system is capable of predicting the heart attack effectively.

关键词： Data mining Heart Disease Frequent Patterns MAFIA(Maximal Frequent Itemset algorithm) id3 algorithm

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A Novel Data Mining Approach for Brain Tumour Detection 2

A Novel Data Mining Approach for Brain Tumour Detection

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2nd IEEE International Conference on Contemporary Computing and Informatics (IC3I)

作者： Kiranmayee, B. V. Rajinikanth, T. V. Nagini, S. VNRVJIET Dept CSE Secunderabad India SNIST Dept IT Hyderabad Telangana India

ISBN: (纸本)9781509052561

Data mining techniques are used for mining useful trends or patterns from textual and image data sets. Medical data mining is very important field as it has significant utility in healthcare domain in the real world. The mining techniques can help the healthcare industry to improve quality of services and grow faster with state-of-the-art technologies. Technology usage is not limited to decision making in enterprises, but spread to every walk of life in all fields. This paper is focused on brain tumour detection which is an essential decision making feature and is a part of healthcare application. This paper proposed a methodology for brain tumour detection which has both training and testing phases. A prototype application has been built to demonstrate the usefulness of the proposed algorithm. The experimental results reveal that the application can be integrated with decision support systems in healthcare domain for improving quality of services.

关键词： Data mining image pre-processing id3 algorithm brain tumour detection Health care industry

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Intelligent Placement Model Based On Decision Tree 9

Intelligent Placement Model Based On Decision Tree

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9th International Conference on Information Technology in Medicine and Education (ITME)

作者： Cai, CongYu Lu, Huijuan Yan, Ke Ye, Minchao China Jiliang Univ Coll Informat Engn Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9781538677438

Recently, the analysis of big data based on artificial intelligence algorithms has shown great potential in the field of education system However, due to the complexity of the education system, the problem of students' placement in primary and secondary schools (matching problem of students and teachers) still exists. This paper proposes a placement model using id3 and C4.5 bagging algorithms. The proposed model uses the characteristics and academic performances of students to train the decision tree and performs appropriate pruning to implement the decision trees. In the experimental results, the proposed method (id3 algorithms and C4.5 algorithm) achieves high matching accuracy.

关键词： Decision tree placement model C4.5 algorithm id3 algorithm bagging

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Nursing Quality Based on Decision Tree algorithm

Nursing Quality Based on Decision Tree Algorithm

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Xue, Yan Qian, Yang Lijuan, Zhuang Yunchun, Ma Zheng, Wang Qujing Medical College Yunnan Province Qujing China

ISBN: (纸本)9798350309614

With the continuous development of commodities and the constant changes in quality, medical care behavior is a commodity in the process of human society, and people are also concerned about its quality. Therefore, nursing quality evaluation has increasingly become the top priority of medical research. In addition, with the continuous development and application of data mining methods, many researchers have applied data mining methods to the study of nursing quality. This paper studies nursing quality based on Decision Tree algorithm (DTA). This paper firstly describes the basic concepts of the data DTA and the process of decision tree (DT) construction, then explains the common algorithms, and finally the model construction and evaluation. © 2023 IEEE.

关键词： Data Mining Decision Tree algorithm id3 algorithm Nursing Quality

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Application of Computer Science and Technology Intelligence Based on Improved Decision Tree algorithm 3

Application of Computer Science and Technology Intelligence ...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Bai, Songlin School of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9798350317022

Data mining is the process of discovering large data sets and revealing hidden laws. This paper aims to study the intelligent application analysis of computer science and technology based on the improved decision tree algorithm. Taking the improvement of the id3 algorithm as the research goal, on the basis of analyzing the tasks of data mining, decision tree algorithm and the application development of computer science and technology, it proposes improved scheme of id3 algorithm is presented and realized. Finally, this paper uses the id3 algorithm and the improved id3 algorithm to conduct a decision tree construction experiment. The experimental results show that compared with the id3 algorithm, the improved algorithm reduces a lot of training logarithms, and therefore shortens the construction of the same training data. The decision tree is time-consuming and greatly improves the computing performance. © 2023 IEEE.

关键词： Computer Science and Technology Decision Tree algorithm id3 algorithm Intelligent Application

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GMM Clustering-Based Decision Trees Considering Fault Rate and Cluster Validity for Analog Circuit Fault Diagnosis

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IEEE ACCESS 2019年 7卷 140637-140650页

作者： Shi, Junyou He, Qingjie Wang, Zili Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Traditional decision trees for fault diagnosis often use an id3 construction algorithm. For promoting the accuracy and efficiency of decision trees, considering the cluster validity and fault rates, this paper proposes two improved trees, CV-DTs and FR-DTs. This paper mainly has two highlights. The first highlight is to propose a CV-DT which is constructed by an improved id3 algorithm considering the cluster validity index. A new cluster validity index which can compare the cluster validities of different attributes is proposed to modify the information gain. This method selects the splitting attributes with higher classification credibility and increases the diagnostic accuracy. The second highlight is to propose an FR-DT which is constructed by an improved id3 algorithm considering the fault rates. This algorithm not only considers the partitioning ability of each attribute, but also considers the isolation priority of faults with higher fault rates. This method decreases the average diagnostic steps and promotes the diagnostic efficiency. Through a simulation case and a real board case, these decision trees are proved to be effective diagnostic tools which have higher accuracies or efficiencies in analog circuit.

关键词： Decision trees Clustering algorithms Circuit faults Classification algorithms Partitioning algorithms Fault diagnosis Indexes Cluster validity decision tree fault rate GMM clustering id3 algorithm

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Application of big data information system in early diagnosis, treatment, and nursing of cervical cancer infected by human papillomavirus

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EXPERT SYSTEMS 2023年第4期40卷 e12791-e12791页

作者： Chen, Guo Zhang, Wenjian Guangzhou Univ Chinese Med Coll Clin Med 1 Jichang Rd 12 Guangzhou Peoples R China

In order to study the application value of big data information monitoring system in early diagnosis, treatment, and nursing of human papillomavirus (HPV) infected cervical cancer, firstly, the traditional id3-based decision tree model was optimized through the minimum sample number of different leaf nodes. According to the optimization model, the diagnosis model of HPV-infected cervical cancer was constructed and applied to the monitoring system of big data of cervical cancer. Diagnosis model and information monitoring system (DMIMS) was compared with Decision tree based on decision support degree (DTBDS). Then, 876 HPV-infected cervical cancer patients diagnosed in our hospital from January 30, 2018 to January 30, 2019 were defined as the experimental group, and 670 HPV-infected cervical cancer patients diagnosed in our hospital from January 30, 2017 to January 30, 2018 were defined as the control group. Only the experimental group used cervical cancer data and information diagnosis system. Finally, the biopsy rate after examination, the coincidence rate with pathology diagnosis, the detection rate of precancerous lesion of HPV infection, and the detection rate of HPV infection of cervical cancer were compared between the two groups. The results showed that the classification accuracy of DMIMS (95%) was higher than that of DTBDS (67%) (p < 0.05). The biopsy rate of the experimental group was significantly lower than that of the control group (p < 0.05);the coincidence rate between biopsy and pathological diagnosis, detection rate of HPV precancerous lesion, and detection rate of HPV infected cervical cancer were significantly higher in the experimental group than in the control group (p < 0.05), which showed that the big data monitoring system of cervical cancer played an important role in the detection of HPV infection cervical cancer, which can make the patients with precancerous lesions and early cervical cancer get timely diagnosis, treatment, and nursing.

关键词： cervical cancer big data information monitoring system decision tree detection rate of precancerous lesions HPV-infected cervical cancer id3 algorithm

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Effective Sentiment Analysis for Opinion Mining Using Artificial Bee Colony Optimization

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Research Journal of Applied Sciences, Engineering and Technology 2016年第8期12卷 828-840页

作者： T.M. Saravanan A. Tamilarasi Department of Computer Applications Kongu Engineering College Perundurai Erode Tamil Nadu 638052 India

Opinions play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. The huge quantity of information on web platforms put together feasible for exercise as data sources, in applications based on opinion mining and classification. An effective sentiment analysis process proposes in this research for mining and classifying the opinions. The phases of the proposed research are: (1) Data Pre-processing Phase (2) Potential Feature Extraction Phase (3) Opinion Extraction and Mining Phase and (4) Opinion Classification Phase. Initially, the datasets from various web documents get preprocessed and gives as part-of-speech tagged text. An Improved High Adjective Count (IHAC) algorithm employs on the Part-Of-Speech tagged text to extract the potential features. Improved High Adjective Count algorithm effectively optimizes the scores of the nouns to extract the potential features. An Artificial Bee Colony (ABC) algorithm works under the IHAC algorithm for providing opinion scores and also for giving ranks for every noun. Max Opinion Score algorithm can be then helpful to extract the opinion words followed by the classification phase, in which, id3 algorithm utilizes to classify the review into three kinds positive, negative and neutral based on the opinions. The implementation is carried out on Customer Review Datasets and Additional Review Datasets with the aid of JAVA platform and also the experimentation results are analyzed.

关键词： Artificial bee colony algorithm id3 algorithm improved high adjective count algorithm max opinion score algorithm opinion mining

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