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Comparison of Machine Learning Methods for Binary Classification of Multicollinearity Data
(2024-12-02)
Araveeporn, Autcha
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Wanitjirattikal, Puntipa
This study examines the effectiveness of binary classification performance in multicollinearity. Four machine learning methods, namely backpropagation neural network, Naïve Bayes, support vector machine, and random forest, are compared in terms of their efficiency in handling multicollinear data. The evaluation of binary classification performance efficiency considers multicollinearity in independent variables, considering both a constant correlation model and the Toeplitz correlation. Correlation coefficients of 0.1 and 0.9 are explored in the analysis. The independent variables in this study are simulated from a multivariate normal distribution with 10, 20, 30, and 40 variables, respectively. The dependent variable is constructed using the logit function with sample sizes of 100 and 200. The simulation and data analysis are performed using the R Studio program and repeated 1,000 times for each scenario. The findings of this research reveal that the backpropagation neural network and Naïve Bayes methods exhibit superior performance in determining the mean accuracy percentage under constant correlation. On the other hand, the backpropagation neural network and support vector machine are the most effective methods in determining the mean accuracy percentage when dealing with multicollinearity in the form of Toeplitz correlation.
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Water Quality Index (WQI) Prediction Using Machine Learning Algorithms
(2023-01-01)
Kularbphettong, Kunyanuth
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Waraporn, Phanu
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Raksuntorn, Nareenart
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Vivhivanives, Rujijan
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Sangsuwon, Chanyapat
Water resources used by human activities ranges typically from personal and household, agricultural, industrial, recreational to environmental pursuits. The effects of these water utilizations are actually of great concerns by many due to various threats created by human functions and the nature itself, for instance, climate change, pollution, scarcity, and even conflicts. To mitigate these threats, implementation of water quality management based on recognized standards and guidelines not only will they provide solid framework and benchmark used in relation to the assessment of the water quality but will also enable the identification of corresponding classification indicated by the water quality index (WQI) pertinent and relevant to the surface water dataset. This paper aims at applying selected predictive modeling techniques that are highly optimized for use in semi-automating the work of the water quality classification (WQC) and the water quality index (WQI) that subsequently can be used in assisting the planning, problem-solving and/or decision-making processes. The preliminary results obtained are quite satisfactory as follow: predicting WQI using neural network model (NN) outperforms both the Multiple Linear Regression (MLR) and the Support Vector Machine (SVM) based on a mean absolute error (MAE) lower than the two models and predicting WQC using SVM, and ANN models based on accuracy score with SVM returns a favorable accuracy score higher than two others.
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Comparisons of SVM Kernels for Insurance Data Clustering
(2022-08-01)
Nurhidayat, Irfan
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Pimpunchat, Busayamas
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Noeiaghdam, Samad
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Fernández-Gámiz, Unai
This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data.
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Automatic Thai Ticket Classification By Using Machine Learning For IT Infrastructure Company
(2022-01-01)
Khowongprasoed, Kraidet
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Titijaroonroj, Taravichet
Ticket classification is a process to define the category name of each ticket before assigning the resolution team to serve each ticket. It is an important process to support the customers inside and outside the company. It can make customer dissatisfaction if the processing time is high or delayed. Based on the recording data in 2019-2021 at the studying company, we found that the manual ticket classification got an error rate about 53 percent because the office workers misunderstand. To alleviate this problem, we propose the methodology for automatic Thai ticket classification by using Term Frequency-Inverse Document Frequency with Support Vector Machine. The experimental result shows that the performance of the proposed methodology is higher than the manual classification by 2 times or 41 percent.
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Equatorial Plasma Bubble Detection by Support Vector Machine at Chumphon Station, Thailand
(2022-01-01)
Thanakulketsarat, Thananphat
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Supnithi, Pornchai
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Myint, Lin Min Min
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Hozumi, Kornyanat
Equatorial Plasma Bubble (EPB) is a phenomenon in which depletion of plasma density occurs in the ionosphere particularly in the equatorial region. It can degrade the performances of the navigation system and satellite communication. In this work, we analyze EPB based on the very-high frequency (VHF) radar images at Chumphon station, Thailand. Then an EPB detection system using the support vector machine (SVM) technique is developed, and the accuracies of the systems using different kernels: linear kernel, the polynomial kernel, the radial basic functions kernel (RBF), and the sigmoid kernel are compared. Among the different kernels, we find that the RBF kernel gives the highest accuracy in prediction at 86.67 percent.
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Classification of Fault Type on Loop-Configuration Transmission System Using Support Vector Machine
(2017-11-15)
Sreewirote, Bancha
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Ngaopitakkul, Atthapol
This paper proposed to applied Support vector machine (SVM) algorithm for classified the fault type on the 500 kV transmission systems with connected in loop configuration. The fault signal was simulated using ATPDraw/EMTP program at frequency 200 kHz. The fault detection was analyzing the high frequency component by discrete wavelet transform (DWT). For the first stage, the coefficient of DWT was used for the fault detection. After the fault can be detected, the fault classification will be identified using SVM algorithm. The maximum coefficient from wavelet transform was used as input pattern of SVM to classify the type of fault. The input pattern of SVM consists of 4 input; maximum coefficient of DWT in all phase current and zero sequence current. For the SVM process, the fault classification used the five model of SVM because each model is working in parallel to avoid mistake (or error). In addition, the same input in five model were simultaneously used while the output of each models is differently according to specification of model. The overall result of 2160 case studies data can be summarized that the fault classification using SVM algorithm is highly satisfactory.
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Edited audio detection using ensemble learning
(2015-02-27)
Suwan, Takdanai
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Jaiyen, Saichon
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Wiangsripanawan, Rungrat
Detecting edited audios is the challenging problem that can help forensic scientists to separate genuine, unedited recording from edited recordings. This paper proposes the technique for detecting edited audios using Ensemble Learning. This problem can be considered as a two-class classification problem which audio data are classified into two classes including edited and unedited audios. The performance of the proposed model is compared with the performance from the Support Vector Machine, Naïve Bayes, Radial Basis Function Neural Network, and Probabilistic Neural Networks. The experimental results demonstrate that the proposed model is the most appropriated method for detecting the edited audios.
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Rule extraction for support vector machine using input space expansion
(2011-12-01)
Pitiranggon, Prasan
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Benjathepanun, Nunthika
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Banditvilai, Somsri
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Boonjing, Veera
Fuzzy Rule-Based System (FRB) in the form of human comprehensible IF-THEN rules can be extracted from Support Vector Machine (SVM) which is regarded as a black-boxed system. We first prove that SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS) are equivalent indicating that SVM's decision can actually be represented by fuzzy IFTHEN rules. We then propose a rule extraction method based on kernel function firing strength and unbounded support vector space expansion. An advantage of our method is the guarantee that the number of final fuzzy IF-THEN rules is equal or less than the number of support vectors in SVM, and it may reveal human comprehensible patterns. We compare our method against SVM using popular benchmark data sets, and the results are comparable. © 2011 Springer-Verlag Berlin Heidelberg.
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Customer failure modes prediction for hard disk drive using neural networks rank-level fusion
(2011-08-12)
Tepin, Waraporn
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Kidjaidure, Yuttana
The Prediction of Customer Failure Modes in Hard Disk Drive (HDD) is proposed using Neural Networks Rank-Level Fusion applied on key parameters measured in the manufacturing process of a HDD. In our methods, Neural Networks, Discriminant Analysis, Bayesian Networks, Support Vector Machines are applied to classified data which was obtained from Principal Component Analysis. The output of the classifiers is further aggregated using Neural Networks Rank-Level Fusion to form the final prediction model. The resultant of the model is a highly accurate prediction superior to Borda Count, Logistic Regression Fusion Methods and beyond current known reliability predictors of HDD failures. © 2011 IEEE.
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Fuzzy rules generation and extraction from support vector machine based on kernel function firing signals
(2010-08-01)
Pitiranggon, Prasan
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Benjathepanun, Nunthika
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Banditvilai, Somsri
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Boonjing, Veera
Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of firing signals of support vectors using Gaussian kernel. The final set of rules is then obtained from the second set through input scatter partitioning. A distinctive advantage of our method is the guarantee that the number of final fuzzy IF- THEN rules is not more than the number of support vectors in the trained SVM. The final FRB system obtained is capable of performing classification with results comparable to its SVM counterpart, but it has an advantage over the black-boxed SVM in that it may reveal human comprehensible patterns.

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