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Business failure prediction based on two-stage selective ensemble with manifold learning algorithm and kernel-based fuzzy self-organizing map

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KNOWLEDGE-BASED SYSTEMS 2017年第Apr.1期121卷 99-110页

作者： Wang, Lu Wu, Chong Harbin Inst Technol Sch Econ & Management Dept Management Sci & Engn West Str 92 Harbin 150001 Peoples R China

More and more models and algorithms are used to predict business failure. Many of them are not suitable for the complicated distribution of financial data, which leads to unsatisfactory prediction performance. The manifold learning algorithm is a valid method to preprocess financial data because of the good performance on dimensionality reduction for any data distribution. The kernel-based method is introduced to improve the disadvantage of fuzzy self-organizing map (FSOM) which is the limitation of spherical data distribution. Therefore, this study adopts manifold learning algorithm to select feature subsets, and employs the kernel-based FSOM (KFSOM) to compose base classifiers, and proposes the two-stage selective ensemble model for business failure prediction (BFP). First, three manifold learning algorithms, which are Isomap, Laplacian Eigenmaps and Locally Linear Embedding, are adopted to select three feature subsets from original financial data. Then, KFSOM uses three kinds of kernel functions respectively, which are Gaussian, Polynomial and Sigmoid, to obtain three classifiers. Hence, three feature subsets are computed by KFSOMs with three kernel functions respectively to acquire nine base classifiers. Last, nine base classifiers are integrated by the two-stage selective ensemble method. In the first stage, nine base classifiers are ranked according to three standards. The stepwise forward selection approach is adopted to selectively integrate nine base classifiers according to different standards. In the second stage, three selective ensembles in the first stage are integrated again to acquire the final result. In the empirical research, this work employs financial data from Chinese listed companies to predict business failure, and makes comparative analysis with previous methods. It is the conclusion that the two-stage selective ensemble with manifold learning algorithm and KFSOM is good at predicting business failure. (C) 2017 Elsevier B.V. All rights reser

关键词： Business failure prediction manifold learning algorithm Kernel-based fuzzy self-organizing map Selective ensemble method

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A Decision Tree Candidate Property Selection Method Based on Improved manifold learning algorithm 10th

A Decision Tree Candidate Property Selection Method Based on...

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10th EAI Annual International Conference on Ad Hoc Networks (AdHocNets)

作者： Guo, Fangfang Chao, Luomeng Wang, Huiqiang Harbin Engn Univ Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9783030058883;9783030058876

When the traditional decision tree algorithm is applied to the field of network security analysis, due to the unreasonable property selection method, the overfitting problem may be caused, and the accuracy of the constructed decision tree is low. Therefore, this paper proposes a decision tree selection method based on improved manifold learning algorithm. The manifold learning algorithm maps the high-dimensional feature space to the low-dimensional space, so the algorithm can acquire the essential attributes of the data source. According to this, the problems of low accuracy and overfitting can be solved. Aiming at the traditional manifold learning algorithms are sensitive to noise and the algorithms converges slowly, this paper proposes a Global and Local Mapping manifold learning algorithm, and this method is used to construct a decision tree. The experimental results show that compared with the traditional ID3 decision tree construction algorithm, the improved method reduces 2.16% and 1.626% in false positive rate and false negative rate respectively.

关键词： Network security Decision tree manifold learning algorithm

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A Novel Method for Hyperspectral Image Classification Based on Laplacian Eigenmap Pixels Distribution-Flow

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2013年第3期6卷 1602-1618页

作者： Hou, Biao Zhang, Xiangrong Ye, Qiang Zheng, Yaoguo Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

The accurate classification of hyperspectral images is an important task for many practical applications. In this paper, a new method for hyperspectral image classification is proposed based on manifold learning algorithm, The approach introduced here presents three major contributions: 1) a new Laplacian eigenmap pixels distribution-flow (LE PD-Flow) is proposed for hyperspectral image analysis, in which, a new joint spatial-pixel characteristics distance (JSPCD) measure is constructed to improve the accuracy of classification and a suitable weighting factor is used to distinguish data points of different classes by combining the spectral feature with the spatial feature;2) the adjustment strategy of each manifold mappings is addressed, which allows not only better visualization of the results, but also the comparisons of mapping results with an appropriate measurement;3) in order to get useful boundary points used for classification, single threshold and multiple thresholds method are presented to solve small scale and large scale classification problem, respectively. We can easily obtain the expected classification results by adjusting the weights of the two kinds of feature of hyperspectral image. With the LE PD-Flow, variation of pixels on the boundaries for classification can be found, and then hyperspectral data can be labeled with high accuracy. Experimental results show that the proposed method is effective for classification of hyperspectral image.

关键词： Classification hyperspectral image Laplacian eigenmap (LE) manifold learning algorithm pixels distribution-flow (PD-Flow)

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Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine

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MEASUREMENT 2013年第10期46卷 4189-4199页

作者： Dong, Shaojiang Tang, Baoping Chen, Renxiang Chongqing Univ State Key Lab Mech Transmiss Chongqing 400030 Peoples R China Chongqing Jiaotong Univ Sch Mechatron & Automot Engn Chongqing 400074 Peoples R China

In order to effectively recognize the bearing running state, a new method based on non-extensive wavelet feature scale entropy and the Morlet wavelet kernel support vector machine (MWSVM) was proposed. Firstly, the gathered vibration signals were decomposed by the wavelet to obtain the corresponding wavelet coefficients. Then, based on the integration of non-extensive entropy and the coefficients, the features were extracted by the wavelet feature scale entropy. However, the extracted features remained high-dimensional and excessive redundant information still existed. Therefore, the manifold learning algorithm locality preserving projection (LPP) was introduced to extract the characteristic features and to reduce the dimension. The extracted characteristic features were inputted into the MWSVM to train and construct the running state identification model;the bearing running state identification was thereby realized. Cases of test and actual fault were analyzed. The results validate the effectiveness of the proposed algorithm. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Non-extensive wavelet feature scale entropy manifold learning algorithm Morlet wavelet kernel support vector machine State recognition

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Local non-linear alignment for non-linear dimensionality reduction

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IET COMPUTER VISION 2017年第5期11卷 331-341页

作者： Niu, Guo Ma, Zhengming Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Foshan Univ Sch Elect Informat Engn Foshan 528000 Peoples R China

In manifold learning, alignment is performed with the objective of deriving the global low-dimensional coordinates of input data from their local coordinates. In virtually all alignment processes, the relation between the local and global coordinates is designed intuitively, without mathematical deduction and detailed analysis. In this study, the authors propose a local non-linear alignment manifold learning algorithm (LNA) for non-linear dimensionality reduction, based on the concept of local pullback and the mathematical characteristics of a manifold. According to mathematical manifold theory, a function defined on a manifold cannot be differentiated directly on the manifold directly. Instead, it has to be pulled back to Euclidean space with the help of local homeomorphism between the manifold and Euclidean space, where it is then differentiated. In the authors' proposed algorithm, the component functions of global homeomorphism are regarded as the functions defined on the manifold and pulled back to the Euclidean space. Then, Taylor expansion is utilised up to the second order to establish the relation between the global and local coordinates. The objective function in LNA is based on the alignment error and can be solved with an eigenvalue problem. The experimental results conducted on various datasets verify the validity of the authors' method.

关键词： learning (artificial intelligence) eigenvalues and eigenfunctions local nonlinear alignment nonlinear dimensionality reduction manifold learning algorithm LNA alignment process local pullback concept mathematical manifold theory Euclidean space local homeomorphism Taylor expansion objective function eigenvalue problem

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Localization in Wireless Sensor Network Using LLE-ISOMAP algorithm

Localization in Wireless Sensor Network Using LLE-ISOMAP Alg...

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IEEE Region 10 Conference (TENCON)

作者： Singh, Krishna Pratap Bhai, Rajat Mishra, Vishrut Nagar, Pradeep Kasinayal, Jyoti IIIT Allahabad Dept Informat Technol Machine Learning & Optimizat Lab Allahabad Uttar Pradesh India

ISBN: (纸本)9781509011346

Node Localization is the major challenge in Wireless Sensor network. The presence of measurement error and noise in the environment makes process of localization more erroneous and complicated. In this paper LLE-Isomap based manifold learning algorithm is used to solve the problem of node localization. Results of LLE-Isomap is compared with various others manifold learning algorithms like MDS, Isomap, LLE method and PLS-Isomap. Results show that LLE-Isomap outperforms to the other techniques in node localization.

关键词： Node Localization manifold learning algorithm LLE PLS PLS-Isomap LLE-Isomap

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Local and nonlocal preserving projection for bearing defect classification and performance assessment

Local and nonlocal preserving projection for bearing defect ...

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IEEE Transactions on Industrial Electronics

作者： Yu, Jianbo School of Mechatronics Engineering and Automation Shanghai University Shanghai 200072 China

The sensitivity of various physical features that are characteristics of bearing performance may vary significantly under different working conditions. Thus, it is critical to extract the most effective information from the original physical features generated from vibration signals for bearing defect classification and performance degradation assessment. This paper proposes a local and nonlocal preserving projection (LNPP)-based feature extraction algorithm, which is different from principal component analysis that aims to discover the global structure of Euclidean space and regular manifold learning algorithms that attempt to preserve local structure in data. LNPP is capable of discovering both local and nonlocal structures of data manifold. This may enable LNPP to find the meaningful low-dimensional information hidden in the high-dimensional feature set and then to serve as a preprocessor for defect classification. Furthermore, an LNPP-based quantification index is proposed for the assessment of bearing performance degradation. An LNPP-based contribution plot for feature selection is developed to improve the degradation detection sensitiveness and to reduce false alarms. A dynamic LNPP for bearing performance assessment is further developed to consider inherent autocorrelation existing in vibration data. Detailed results are very promising and are reported in this paper. This paper will provide guidance for the applications of manifold learning algorithms (e.g., LNPP) in machine fault diagnosis and performance prognostics. © 2006 IEEE.

关键词： Principal component analysis Bearings (structural) Degradation Feature extraction learning algorithms Bearing defect Bearing performance Contribution plots Data manifolds defect classification Euclidean spaces False alarms Feature extraction algorithms Feature sets Global structure High dimensional Local structure Machine fault diagnosis manifold learning manifold learning algorithm Nonlocal Performance assessment Performance degradation assessment Physical features Vibration data Vibration signal Working conditions

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