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A C4.5 algorithm for english emotional classification

EVOLVING SYSTEMS 2019年第3期10卷 425-451页

作者： Phu Vo Ngoc Chau Vo Thi Ngoc Tran Vo Thi Ngoc Dat Nguyen Duy DTU Inst Res & Dev Da Nang Vietnam Ho Chi Minh City Univ Technol HCMUT CSE Ho Chi Minh City Vietnam Vietnam Natl Univ SIM Ho Chi Minh City Univ Technol HCMUT Ho Chi Minh City Vietnam Ly Tu Trong Tech Coll Fac Informat Technol Ho Chi Minh City Vietnam

The solutions for processing sentiment analysis are very important and very helpful for many researchers, many applications, etc. This new model has been proposed in this paper, used in the English document-level sentiment classification. In this research, we propose a new model using C4.5 algorithm of a decision tree to classify semantics (positive, negative, neutral) for the English documents. Our English training data set has 140,000 English sentences, including 70,000 English positive sentences and 70,000 English negative sentences. We use the C4.5 algorithm on the 70,000 English positive sentences to generate a decision tree and many association rules of the positive polarity are created by the decision tree. We also use the C4.5 algorithm on the 70,000 English negative sentences to generate a decision tree and many association rules of the negative polarity are created by the decision tree. Classifying sentiments of one English document is identified based on the association rules of the positive polarity and the negative polarity. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. We have tested our new model on our testing data set and we have achieved 60.3% accuracy of sentiment classification on this English testing data set.

关键词： Sentiment classification English sentiment classification English document opinion mining C4 5 algorithm c4 5 CA Decision tree

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Research and improvement of feature detection algorithm based on FAST

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RENDICONTI LINCEI-SCIENZE FISICHE E NATURALI 2021年第4期32卷 775-789页

作者： Li, Yulin Zheng, Wenfeng Liu, Xiangjun Mou, Yuanyuan Yin, Lirong Yang, Bo Xian Fanyi Univ Sch Innovat & Entrepreneurship Xian Peoples R China Univ Elect Sci & Technol China Sch Automat Chengdu 610054 Peoples R China Louisiana State Univ Dept Geog & Anthropol Baton Rouge LA 70803 USA

At present, in the application of feature-based medical 3D reconstruction technology, there are still problems such as low matching accuracy of feature points in endoscope images and slow processing speed of image data. Therefore, the feature-based 3D reconstruction theory is of great research value and has great application value. This paper proposed a new feature detection method to improve the problems. This paper divides feature detection into two parts for further improvements: feature extraction and feature description. For feature extraction, the FAST algorithm shows a poor classification effect, so this paper adds the decision tree based on the C4.5 algorithm into the traditional FAST. The original data are divided into two decision trees to make the feature extraction performance more stable and feature point extraction more efficient. For the feature description part, the FREAK descriptor is used, combined with this paper's improved feature extraction algorithm. The feature points are extracted in scale space. The second-order function fitting is carried out according to the feature points' response scores in different scales. The scale-invariant descriptor of sub-pixel precision is obtained. The experimental results on the endoscope image show that the feature extraction method has a higher extraction accuracy and faster extraction speed. In addition, the feature description algorithm has higher calculation efficiency.

关键词： S Feature detection FAST feature extraction algorithm C4 5 algorithm FREAK descriptor

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Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques

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ENGINEERING WITH COMPUTERS 2019年第2期35卷 659-675页

作者： Faradonbeh, Roohollah Shirani Taheri, Abbas Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia

Rockburst phenomenon is the extreme release of strain energy stored in surrounding rock mass which could lead to casualties, damage to underground structures and equipment and finally endanger the economic viability of the project. Considering the complex mechanism of rockburst and a large number of factors affecting it, the conventional criteria cannot be used generally and with high reliability. Hence, there is a need to develop new models with high accuracy and ease to use in practice. This study focuses on the applicability of three novel data mining techniques including emotional neural network (ENN), gene expression programming (GEP), and decision tree-based C4.5 algorithm along with five conventional criteria to predict the occurrence of rockburst in a binary condition. To do so, a total of 134 rockburst events were compiled from various case studies and the models were established based on training datasets and input parameters of maximum tangential stress, uniaxial tensile strength, uniaxial compressive strength, and elastic energy index. The prediction strength of the constructed models was evaluated by feeding the testing datasets to the models and measuring the indices of root mean squared error (RMSE) and percentage of the successful prediction (PSP). The results showed the high accuracy and applicability of all three new models;however, the GA-ENN and the GEP methods outperformed the C4.5 method. Besides, it was found that the criterion of elastic energy index (EEI) is more accurate among other conventional criteria and with the results similar to the C4.5 model, can be used easily in practical applications. Finally, a sensitivity analysis was carried out and the maximum tangential stress was identified as the most influential parameter, which could be a guide for rockburst prediction.

关键词： Rockburst occurrence Data mining techniques Emotional neural network Gene expression programming C4 5 algorithm Conventional criteria

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Research on optimization and application of Spark decision tree algorithm under cloud-edge collaboration

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第11期37卷 8833-8854页

作者： Wang, Suzhen Jia, Zhiting Cao, Ning Hebei Univ Econ & Business Shijiazhuang Hebei Peoples R China Wuxi Vocat Coll Sci & Technol Sch Artificial Intelligence Wuxi Jiangsu Peoples R China

This paper optimizes and parallelizes the C4.5 algorithm on the Spark platform under cloud-edge collaboration. First, the information entropy calculation in the definition of boundary points and Fayyad's theorem is improved by the Gini index, which reduces the number of information entropy calculations for selecting segmentation points in the traditional C4.5 algorithm continuous attribute discretization operation and simplifies the calculation formula, thus reducing the execution time of the algorithm. On this basis, the CFS algorithm is introduced to optimize the calculation of the information gain ratio, to facilitate the selection of better attributes for decision tree partitioning. The improved C4.5 algorithm is then parallelized in Spark platform. In this paper, we choose the "intelligent door guard" of cloud-edge collaboration under the epidemic prevention and control management context as the application scenario for experimental verification and make timely risk assessment for those who want to enter the place and give them a response whether they are allowed to enter or not. Experiments show that the improved parallel C4.5 algorithm reduces the running time and raises the accuracy of the algorithm, compared with the traditional C4.5 algorithm and previous improvements to it. In the application scenario of cloud-edge collaboration, the improved algorithm in this paper decreases the time delay and increases the inspection efficiency.

关键词： C4 5 algorithm cloud-edge collaboration decision tree algorithm edge computing information gain ratio

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Using Decision Tree Classifiers in Source Code Analysis to Recognize algorithms: An Experiment with Sorting algorithms

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COMPUTER JOURNAL 2011年第11期54卷 1845-1860页

作者： Taherkhani, Ahmad Aalto Univ Dept Comp Sci & Engn FI-00076 Aalto Finland

We discuss algorithm recognition (AR) and present a method for recognizing algorithms automatically from Java source code. The method consists of two phases. In the first phase, the recognizable algorithms are converted into the vectors of characteristics, which are computed based on static analysis of program code, including various statistics of language constructs and analysis of Roles of Variables in the target program. In the second phase, the algorithms are classified based on these vectors using the C4.5 decision tree classifier. We demonstrate the performance of the method by applying it to sorting algorithms. Using leave-one-out cross-validation technique, we have conducted an experimental evaluation of the classification performance showing that the average classification accuracy is 98.1% (the data set consisted of five different types of sorting algorithms). The results show the applicability and usefulness of roles of variables in AR, and illustrate that the C4.5 algorithm is a suitable decision tree classifier for our purpose. The limitations of the method are also discussed.

关键词： algorithm recognition decision tree classifier C4 5 algorithm roles of variables

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Decentralised intelligent transport system with distributed intelligence based on classification techniques

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IET INTELLIGENT TRANSPORT SYSTEMS 2016年第10期10卷 674-682页

作者： Osaba, Eneko Onieva, Enrique Moreno, Asier Lopez-Garcia, Pedro Perallos, Asier Bringas, Pablo G. Univ Deusto Deusto Inst Technol DeustoTech Ave Univ 24 Bilbao 48007 Spain

This study is focused on a decentralised intelligent transportation systems with distributed intelligence based on classification techniques. The rationale behind this architecture is to offer a fully distributed, flexible and scalable system. The architecture encompasses the entire process of capture and management of available road data, enabling the generation of services to promote transportation efficiency. Besides that, thanks to the embedded classification techniques, the system is capable of predicting and reacting to certain events, facing them in an appropriate way. The aim of this work is to demonstrate how the system works in two different real-world use cases. To achieve this objective, how the architecture acts to deal with some incidences is proven. In addition, both use cases serve to show the effective communication between the different components of the system. Besides this, this work demonstrates the fundamental role played by the artificial intelligence techniques working in the system. The well-known C4.5 algorithm has been used for the accurate prediction of traffic congestion and pollution level. The authors explain in this work the reasons for using this classification technique, and the previous experiments performed.

关键词： traffic engineering computing distributed processing artificial intelligence pattern classification intelligent transportation systems decentralised intelligent transport system distributed intelligence classification techniques available road data management available road data capture artificial intelligence techniques C4 5 algorithm traffic congestion prediction pollution level prediction

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Efficient Utilization of Missing Data in Cost-Sensitive Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第6期33卷 2425-2436页

作者： Zhu, Xiaofeng Yang, Jianye Zhang, Chengyuan Zhang, Shichao Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China

Different from previous imputation methods which impute missing values in the incomplete samples by using the information in the complete samples, this paper proposes a Date-drive Incremental imputation Model, DIM for short, which uses all available information in the data set to impute missing values economically, effectively, orderly, and iteratively. To this end, we propose a scoring rule to rank the missing features by taking into account both the economical criterion and the effective imputation information. The economical criterion takes both the imputation cost and the discriminative ability of the feature into account, while the effective imputation information enables to use all observed information in the data set including the imputed missing values to impute the left missing values. During the imputation process, our DIM first detects the neednot-impute samples for reducing the imputation cost and noise, and then selects the missing features with the top rank to impute first. The imputation process orderly imputes the missing features until all missing values are imputed or the imputation cost is exhausted. Experimental results on UCI data sets demonstrated the advantages of our proposed DIM, compared to the comparison methods, in terms of prediction accuracy and classification accuracy.

关键词： Data models Analytical models Machine learning Decision trees Machine learning algorithms Knowledge discovery Computer science Missing data imputation cost-sensitive learning decision tree classification imputation order C4 5 algorithm imputation cost

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Breast cancer diagnosis using Stochastic Self-Organizing Map and Enlarge C4.5

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第12期82卷 18059-18076页

作者： Jaiswal, Arvind Kumar, Rajeev Acropolis Inst Technol & Res Indore Madhya Pradesh India GL Bajaj Inst Technol & Management Greater Noida Uttar Pradesh India

Timely and accurate Breast Cancer (BC) prediction allows healthcare providers and doctors to take suitable decisions to treat the patients. Thus, this study employed a strategy based on Deep Learning (DL) to diagnose BC. The study intends to cluster the BC data by the proposed Stochastic Self-Organizing Map (SOM) as it has the ability to process complex data. On the other hand, Enlarge C4.5 (E-C4.5) algorithm is introduced to predict the BC cases based on the clustered outcomes. The BC dataset is loaded and pre-processing is performed where dimensionality reduction is executed to select only the relevant features for clustering. This process eases the clustering. Then, clustering is undertaken by the proposed Stochastic Self-Organizing Map. Here, all the identical data are grouped as clusters which make it easy for prediction. Followed by this, Breast Cancer is predicted by the proposed Enlarge-C4.5 algorithm. After this, the predicted results are analysed by comparative analysis through the four standard performance metrics. This analysis is significant as it shows the degree to which the introduced techniques are effective than the existing techniques. The histogram, correlation map, confusion matrix and clustering results are also discussed clearly. The analytical outcomes explore that the proposed methods are effective than the conventional methods as the proposed method shows a high accuracy rate, precision rate, recall rate and F1-score rate. The misinterpretation rate is also found to be minimum on implementing the proposed method, which is confirmed through the confusion matrix. The proposed method also determines the malignant and benign counts.

关键词： Breast cancer Prediction Dimensionality reduction Stochastic Self-organizing Map and Enlarge C4 5 algorithm

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A NOVEL CLASSIFICATION METHOD FOR TROPICAL CYCLONE INTENSITY CHANGE ANALYSIS BASED ON HIERARCHICAL PARTICLE SWARM OPTIMIZATION algorithm

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Journal of Tropical Meteorology 2017年第1期23卷 113-120页

作者：耿焕同孙家清张伟吴正雪 School of Computer and software Nanjing University of Information Science and TechnologyNanjing 210044 China College of Atmospheric Science Nanjing University of Information Science and Technology Nanjing 210044 China

Based on the tropical cyclone(TC) observations in the western North Pacific from 2000 to 2008, this paper adopts the particle swarm optimization(PSO) algorithm of evolutionary computation to optimize one comprehensive classification rule, and apply the optimized classification rule to the forecasting of TC intensity change. In the process of the optimization, the strategy of hierarchical pruning has been adopted in the PSO algorithm to narrow the search area,and thus to enhance the local search ability, i.e. hierarchical PSO algorithm. The TC intensity classification rule involves core attributes including 12-HMWS, MPI, and Rainrate which play vital roles in TC intensity change. The testing accuracy using the new mined rule by hierarchical PSO algorithm reaches 89.6%. The current study shows that the novel classification method for TC intensity change analysis based on hierarchic PSO algorithm is not only easy to explain the source of rule core attributes, but also has great potential to improve the forecasting of TC intensity change.

关键词： tropical cyclone intensity hierarchical PSO algorithm classification and forecasting C4 5 algorithm

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Fault mode prediction based on decision tree

Fault mode prediction based on decision tree

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Yasenjiang·Jiarula Jianmin Gao Zhiyong Gao Hongquan Jiang Rongxi Wang State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University School of Mechanical Engineering Xinjiang University

ISBN: (纸本)9781509001668

Chemical industry enterprise is a large scale of production system compounded with complex and large electromechanical *** the process industry system,the coupling relationship between each devices are closely *** the fault diagnosis for this kind of special complex equipment group is very *** failure mode is not easy to predict because of the consistency between the *** adopt the single equipment fault diagnosis methods tend to go wrong,so we must adopt new methods,or change the way we *** this paper the model of fault mode prediction is put forward firstly and data classification methods based on decision tree and C4.5 algorithm are studied in this paper.A novel method of fault mode prediction based on decision tree is proposed and the processing is described in *** advantage of this prediction method is proved through experiments.

关键词： process industry fault mode prediction decision tree C4 5 algorithm

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