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Improving the Input of Classified Neural Networks Through feature construction

Journal of Systems Engineering and Electronics 2001年第3期12卷 85-89页

作者： Yang, L. Yu, Z. Huang, L. School of Economics and Management Tongji University Shanghai 200092 China

A general classification algorithm of neural networks is unable to obtain satisfied results because of the uncertain problems existing among the features in moot classification programs, such as interaction. With new features constructed by optimizing decision trees of examples, the input of neural networks is improved and an optimized classification algorithm based on neural networks is presented. A concept of dispersion of a classification program is also introduced too in this paper. At the end of the paper, an analysis is made with an example.

关键词： feature construction Neural networks Dispersion Decision trees Hyperplane.

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A New Road Extraction Method from Satellite Images Using Genetic Programming

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Guidance, Navigation and Control 2024年第2期4卷 47-79页

作者： Jing Liang Yaxin Chang Ying Bi Caitong Yue Boyang Qu Mengnan Liu School of Electrical and Information Engineering Zhengzhou University ZhengzhouP.R.China School of Electrical Engineering and Automation Henan Institute of Technology XinxiangP.R.China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang P.R.China School of Electronic and Information Zhongyuan University of Technology ZhengzhouP.R.China State Key Laboratory of Power System of Tractor Luoyang P.R.China

Extracting roads from satellite images is an important task in thefield of computer vision with a wide range of ***,efficient road extraction from satellite images remains a complex challenge due to issues such as data labeling and the diversity of road *** methods often struggle to balance accuracy,robustness,and *** programming(GP)is based on aflexible and interpretable structure that is robust and does not require a large amount of data *** position the road extraction problem as a binary semantic segmentation task and introduce GP ***,an approach for extracting pixel neighborhood features is proposed,and features from multiple images in the DeepGlobe road extraction dataset are ***,an advanced feature construction method based on GP is ***,these advanced features are utilized for training classifier and classi-fication to achieve road *** have validated the effectiveness of the approach on the DeepGlobe road extraction *** results demonstrated that the proposed approach exhibits superior performance compared to traditional classification methods and multilayer perceptron(MLP)in terms of accuracy,generalization,and *** study provides a valuable reference for the integration of GP into the domain of road extraction from satellite images,showcasing their potential to enhance the accuracy and efficiency.

关键词： Road extraction genetic programming feature construction

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Subdivision surface modeling system based on arbitrary topological curves network and combined subdivision

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Journal of Shanghai University(English Edition) 2011年第6期15卷 557-561页

作者：何钢朱灯林李堑张敏 College of Mechanical and Electrical Engineering Hohai University

Arbitrary topological curve network has no restriction in topology structure,so it has more powerful representing ability in defining complex surfaces.A complex surface modeling system is presented based on arbitrary topological curve network and the improved combined subdivision method,its functions including creating and editing curve network,and generating and modifying curve network＇s interpolated *** modeling system can be used to the process of products＇concept design,and its applications is also significant to the development of subdivision method.

关键词： arbitrary topological curve network surface modeling system nonuniform combined subdivision feature construction

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Improving Churn Prediction in Telecommunications using Complementary Fusion of Multilayer features based on Factorization and construction

Improving Churn Prediction in Telecommunications using Compl...

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第26届中国控制与决策会议

作者： Qiuhua Shen Hong Li Qin Liao Wei Zhang KONE KALILOU School of Information Science and Engineering Central South University School Of Architecture Hunan University

ISBN: (纸本)9781479937097

High dimensional and unbalanced datasets are the main problems which prevent from achieving ideally churn prediction performance. features selection is necessary to be adopted to enhance the model performance. A new predicting framework is proposed in this paper which uses complementary fusion of the multilayer features. Several subsets and new features were acquired according to feature factorization and feature construction respectively. The effective features were selected by multilayer complementary fusion which according to the contribution of feature subsets and new features. In this way, the imbalance defects of class distributions can be fixed, prediction accuracy can be improved and system stability can be reinforced. Five data mining models were applied in customer churn. Experimental results demonstrated that the method we proposed could preferably overcome the inelasticity of traditional feature selection algorithms, and more effective than those existing methods in telecommunications industry. Furthermore, we found optimal fusion with prediction model for customer churn prediction in telecommunications industry through exploring the advantages and limitations of each feature subset and prediction techniques.

关键词： Customer Churn Prediction Prediction techniques feature Selection Complementary Fusion feature Factorization feature construction

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