A NOVEL FIVE-CATEGORY LOAN-RISK EVALUATION MODEL USING MULTICLASS LS-SVM BY PSO

2012 ◽  
Vol 11 (04) ◽  
pp. 857-874 ◽  
Author(s):  
JIE CAO ◽  
HONGKE LU ◽  
WEIWEI WANG ◽  
JIAN WANG
Keyword(s):  
Financial Regulation ◽  
Evaluation Model ◽  
Least Square ◽  
Support Vector ◽  
Accuracy Rate ◽  
Rural Credit ◽  
Proposed Model ◽  
Loan Risk ◽  
Regulation Approach ◽  
Risk Evaluation Model

Five-category loan classification (FCLC) is an international financial regulation approach. Recently, the application and implementation of FCLC in the Chinese microfinance bank has mostly relied on subjective judgment, and it is difficult to control and lower loan risk. In view of this, this paper is dedicated to researching and solving this problem by constructing the FCLC model based on improved particle-swarm optimization (PSO) and the multiclass, least-square, support-vector machine (LS-SVM). First, LS-SVM is the extension of SVM, which is proposed to achieve multiclass classification. Then, improved PSO is employed to determine the parameters of multiclass LS-SVM for improving classification accuracy. Finally, some experiments are carried out based on rural credit cooperative data to demonstrate the performance of our proposed model. The results show that the proposed model makes a distinct improvement in the accuracy rate compared with one-vs.-one (1-v-1) LS-SVM, one-vs.-rest (1-v-r) LS-SVM, 1-v-1 SVM, and 1-v-r SVM. In addition, it is an effective tool in solving the problem of loan-risk rating.

scholarly journals A Methodology for Predicting Aggregate Flight Departure Delays in Airports Based on Supervised Learning

2020 ◽  
Vol 12 (7) ◽  
pp. 2749 ◽  
Author(s):  
Bojia Ye ◽  
Bo Liu ◽  
Yong Tian ◽  
Lili Wan
Keyword(s):  
Supervised Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Accuracy Rate ◽  
Forecast Horizon ◽  
Learning Methods ◽  
Extremely Randomized Trees ◽  
Proposed Model ◽  
Operational Data

This paper proposes a new methodology for predicting aggregate flight departure delays in airports by exploring supervised learning methods. Individual flight data and meteorological information were processed to obtain four types of airport-related aggregate characteristics for prediction modeling. The expected departure delays in airports is selected as the prediction target while four popular supervised learning methods: multiple linear regression, a support vector machine, extremely randomized trees and LightGBM are investigated to improve the predictability and accuracy of the model. The proposed model is trained and validated using operational data from March 2017 to February 2018 for the Nanjing Lukou International Airport in China. The results show that for a 1-h forecast horizon, the LightGBM model provides the best result, giving a 0.8655 accuracy rate with a 6.65 min mean absolute error, which is 1.83 min less than results from previous research. The importance of aggregate characteristics and example validation are also studied.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

Model-Based EEG Analysis: Proposal and Verification of Mathematical Model for Application to Neurotechnology

2019 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Mathematical Model ◽  
Binary Classification ◽  
Least Square ◽  
Mental Condition ◽  
Support Vector ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Method ◽  
Model Based ◽  
Proposed Model

Abstract We have modeled dynamics of EEG with one degree of freedom nonlinear oscillator and examined the relationship between mental state of humans and model parameters simulating behavior of EEG. At the IMECE conference last year, Our analysis method identified model parameters sequentially so as to match the waveform of experimental EEG data of the alpha band using one second running window. Results of temporal variation of model parameters suggested that the mental condition such as degree of concentration could be directly observed from the dynamics of EEG signal. The method of identifying the model parameters in accordance with the EEG waveform is effective in examining the dynamics of EEG strictly, but it is not suitable for practical use because the analysis (parameter identification) takes a long time. Therefore, the purpose of this study is to test the proposed model-based analysis method for general application as a neurotechnology. The mathematical model used in neuroscience was improved for practical use, and the test was conducted with the cooperation of four subjects. model parameters were experimentally identified approximately every one second by using least square method. We solved a binary classification problem of model parameters using Support Vector Machine. Results show that our proposed model-based EEG analysis is able to discriminate concentration states in various tasks with an accuracy of over 80%.

scholarly journals On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yong Yang ◽  
Shuaishuai Zheng ◽  
Zhilu Ai ◽  
Mohammad Mahdi Molla Jafari
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Biogas Production ◽  
Input Parameter ◽  
Machine Learning Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Robust Algorithms ◽  
Inference System ◽  
Proposed Model

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.

An Artificial Intelligent Centered Object Inspection System Using Crucial Images

2018 ◽  
Vol 5 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Santosh Kumar Sahoo ◽  
B. B. Choudhury
Keyword(s):  
Principal Component ◽  
Cuckoo Search ◽  
Basic Function ◽  
Least Square ◽  
Support Vector ◽  
Inspection System ◽  
Bench Mark ◽  
Test Functions ◽  
Proposed Model ◽  
Object Inspection

This article proposes a unique optimization algorithm like Adaptive Cuckoo Search (AdCS) algorithm followed by an Intrinsic Discriminant Analysis (IDA) to design an intelligent object classifier for inspection of defective object like bottle in a manufacturing unit. By using this methodology the response time is very faster than the other techniques. The projected scheme is authenticated using different bench mark test functions along with an effective inspection procedure for identification of bottle by using AdCS, Principal-Component-Analysis (PCA) and IDA. Due to this the projected procedures terms as PCA+IDA for dimension reduction in addition to this AdCS-IDA for classification or identification of defective bottles. The analyzed response obtained from by an application of AdCS algorithm followed by IDA and compared to other algorithm like Least-Square-Support-Vector-Machine (LSSVM), Linear Kernel Radial-Basic-Function (RBF) to the proposed model, the earlier applied scheme reveals the remarkable performance.

Driving Behavior Evaluation Model Base on Big Data From Internet of Vehicles

2019 ◽  
Vol 10 (4) ◽  
pp. 78-95 ◽  
Author(s):  
Ruru Hao ◽  
Hangzheng Yang ◽  
Zhou Zhou
Keyword(s):  
Fuel Consumption ◽  
Evaluation Model ◽  
Driving Behavior ◽  
Characteristic Parameters ◽  
Accuracy Rate ◽  
Operating Data ◽  
Model Base ◽  
Proposed Model ◽  
Consumption Data ◽  
Vehicle Networking

This article attempts to evaluate whether a driving behavior is fuel-efficient. To solve this problem, a driving behavior evaluation model was proposed in this article. First, the operating data and fuel consumption data of five trucks were obtained from the vehicle networking system. Four characteristic parameters, which are closely related to fuel consumption, were extracted from 19 sets of vehicle operating data. Then, K-means clustering combined with DBSCAN was adopted to cluster the four characteristic parameters into different driving behaviors. Three types of driving behavior were labeled respectively as low, medium and high fuel consumption driving behavior after clustering analysis. The clustering accuracy rate reached 79.7%. Finally, a fuel consumption-oriented driving behavior evaluation model was established. The model was trained with the labeled samples. The trained model can evaluate the driving behavior online and gives an evaluation of whether the driving behavior is fuel-efficient. The test results show that the prediction accuracy rate of the proposed model can reach to 77.13%.

Study on Safety Risk Evaluation of High-Way Under-Passing Railroad

2013 ◽  
Vol 838-841 ◽  
pp. 1263-1267
Author(s):  
Ke Xu ◽  
Hui Min Li ◽  
Sha Sha Lu
Keyword(s):  
Scientific Method ◽  
Risk Evaluation ◽  
High Speed ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Support Vector ◽  
Safety Risk ◽  
Sample Data ◽  
Current Stage ◽  
Risk Evaluation Model

It is inevitable to make the new-built highways under-pass the existing railroad by virtue of high-speed development of the transportation in China. Since the relevant surveys on the safety risk evaluation of this kind of the project are lacked at this current stage as well as the limited referential sample data, the survey is to research the factors of safety risk and the self-characteristics as well as to build up the safety risk evaluation model by means of support vector machine plus the analysis with the help of the project example. It turns out that the analyzed result by means of the model has the propinquity with the expected result, as means the model with the limited samples is characterized of improving the objective correctness of the evaluation result in order to supply a scientific method for the safety evaluation of this kind of projects.

scholarly journals Crack Forms Sensitivity-Based Prediction on Subsurface Cracks Depth in Ultrasonic-Vibration-Assisted Grinding of Optical Glasses

2021 ◽  
Vol 11 (16) ◽  
pp. 7553
Author(s):  
Peiyi Zhao ◽  
Lei Zhang ◽  
Xianli Liu
Keyword(s):  
Ultrasonic Vibration ◽  
Least Square ◽  
Effective Control ◽  
Support Vector ◽  
Prediction Errors ◽  
Optical Glasses ◽  
Subsurface Cracks ◽  
Proposed Model ◽  
Grey Relational ◽  
Ultrasonic Vibration Assisted Grinding

Subsurface cracks in ultrasonic-vibration-assisted grinding (UVAG) of optical glasses often exhibit diverse forms and proportions. Due to the variety of loads involved in crack formation and propagation, the crack forms and propagation depths have different sensitivities to each process parameter. Predicting the maximum subsurface cracks depth (MSSCD) by considering the varying effects of process parameters plays a key role in implementing effective control of the UVAG process. In this work, the subsurface crack forms and their proportions are investigated by conducting 40 sets of UVAG experiments. The varying effects of the grinding and ultrasonic parameters on the crack form proportions are unveiled by using grey relational analysis. The weighted least square support vector machine (WLS-SVM) prediction model for the MSSCD was developed. Twelve sets of UVAG experiments were carried out to validate the proposed model. The results show that arc-shaped cracks and bifurcated cracks account for 72.5% of all cracks, while ultrasonic vibration amplitude influences most of the proportions of arc-shaped and bifurcated cracks. Compared to other widely used prediction methods, the maximum and average relative prediction errors of the proposed model are 10.54% and 5.59%, respectively, which proves the high prediction accuracy of the model.

Wind Power Ramp Forecasting Based on Least-Square Support Vector Machine

2014 ◽  
Vol 535 ◽  
pp. 162-166 ◽  
Author(s):  
Di Gan ◽  
De Ping Ke
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Least Square ◽  
Support Vector ◽  
Proposed Model ◽  
Ramp Events ◽  
Definition Of ◽  
Short Time

Wind power ramp forecasting is very significant for grid integration of large wind energy. A ramp event is defined as the sharp increase or decrease of wind power on a large scale in short time. A methodology for wind power ramp forecasting is described. The method is based on Least Square Support Vector Machine (LSSVM) and the definition of ramp events by filtering the original signal. The performance of the proposed model is evaluated on a wind farm in China, which shows that LSSVM model is competent in forecasting wind power ramp events.

scholarly journals Three-Class Mammogram Classification Based on Descriptive CNN Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
M. Mohsin Jadoon ◽  
Qianni Zhang ◽  
Ihsan Ul Haq ◽  
Sharjeel Butt ◽  
Adeel Jadoon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Curvelet Transform ◽  
Data Matrix ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Accuracy Rate ◽  
Data Set ◽  
Proposed Model ◽  
Mammogram Images

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

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