scholarly journals Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

2021 ◽  
Vol 2 (4) ◽  
pp. 285-292
Author(s):  
Sugiyarto Surono ◽  
Tia Nursofiyani ◽  
Annisa E. Haryati
Keyword(s):  
Support Vector Machine ◽  
Performance Evaluation ◽  
Membership Function ◽  
Evaluation Model ◽  
Distance Matrix ◽  
Support Vector ◽  
Complex Data ◽  
Fuzzy Support Vector Machine ◽  
Optimization Function ◽  
The Impact

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. SVM is considered as an effective method of data classification, as opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise. One of the techniques used to overcome this inefficiency is fuzzy logic with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies (Xiao Kang et al., 2018). The result further showed that using FSVM with Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PDF

scholarly journals A New Comprehensive Evaluation Method for Water Quality: Improved Fuzzy Support Vector Machine

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1303 ◽  
Author(s):  
Wei Shan ◽  
Shensheng Cai ◽  
Chen Liu
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Membership Function ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Domestic Water ◽  
Machine Method ◽  
The Government

With the pressure of population growth and environmental pollution, it is particularly important to develop and utilize water resources more rationally, safely, and efficiently. Due to safety concerns, the government today adopts a pessimistic method, single factor assessment, for the evaluation of domestic water quality. At the same time, however, it is impossible to grasp the timely comprehensive pollution status of each area, so effective measures cannot be taken in time to reverse or at least alleviate its deterioration. Thus, the main propose of this paper is to establish a comprehensive evaluation model of water quality, which can provide the managers with timely information of water pollution in various regions. After considering various evaluation methods, this paper finally decided to use the fuzzy support vector machine method (FSVM) to establish the model that is mentioned above. The FSVM method is formed by applying the membership function to the support vector machine. However, the existing membership functions have some shortcomings, so after some improvements in these functions, a new membership function is finally formed. The model is then tested on the artificial data, UCI dataset, and water quality evaluation historical data. The results show that the improvement is meaningful, the improved fuzzy support vector machine has good performance, and it can deal with noise and outliers well. Thus, the model can completely solve the problem of comprehensive evaluation of water quality.

Study on Housing Performance Evaluation Model Based on Hierarchical Potential Support Vector Machine

2011 ◽  
Vol 90-93 ◽  
pp. 894-898
Author(s):  
Bo Jing Tian
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Performance Evaluation ◽  
Decision Tree ◽  
Qualitative Analysis ◽  
Supplier Selection ◽  
Evaluation Model ◽  
Support Vector ◽  
Performance Evaluation Model ◽  
Multi Class Classification

Housing performance is an important and widely studied topic since it has significant impact on architecture design and programming. In terms of problems existing in the field, a new support vector machine technology, potential support vector machine, is introduced and then combined with decision tree to address issues on supplier selection including feature selection, multi-class classification and so on. And the methodology proposed in the paper, which is proved to the strengthens of integrating knowledge and experiences from experts in the paper, can be applied in housing performance evaluation which is one of complex issues combined with processes including not only quantitative, but also qualitative analysis.

scholarly journals PREDICTING PRODUCTIVITY LOSS CAUSED BY CHANGE ORDERS USING THE EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINE INFERENCE MODEL

2015 ◽  
Vol 21 (7) ◽  
pp. 881-892 ◽  
Author(s):  
Min-Yuan Cheng ◽  
Dedy Kurniawan Wibowo ◽  
Doddy Prayogo ◽  
Andreas F. V. Roy
Keyword(s):  
Support Vector Machine ◽  
Construction Projects ◽  
Productivity Loss ◽  
Support Vector ◽  
Model Parameters ◽  
Inference Model ◽  
Fuzzy Support Vector Machine ◽  
Change Orders ◽  
Artificial Neural Network Ann ◽  
The Impact

Change orders in construction projects are very common and result in negative impacts on various project facets. The impact of change orders on labor productivity is particularly difficult to quantify. Traditional approaches are inadequate to calculate the complex input-output relationship necessary to measure the effect of change orders. This study develops the Evolutionary Fuzzy Support Vector Machines Inference Model (EFSIM) to more accurately predict change-order-related productivity losses. The EFSIM is an AI-based tool that combines fuzzy logic (FL), support vector machine (SVM), and fast messy genetic algorithm (fmGA). The SVM is utilized as a supervised learning technique to solve classification and regression problems; the FL is used to quantify vagueness and uncertainty; and the fmGA is applied to optimize model parameters. A case study is presented to demonstrate and validate EFSIM performance. Simulation results and our validation against previous studies demonstrate that the EFSIM predicts the impact of change orders significantly better than other AI-based tools including the artificial neural network (ANN), support vector machine (SVM), and evolutionary support vector machine inference model (ESIM).

scholarly journals A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

2020 ◽  
Vol 10 (3) ◽  
pp. 1065 ◽  
Author(s):  
Wei Liu ◽  
LinLin Ci ◽  
LiPing Liu
Keyword(s):  
Support Vector Machine ◽  
Membership Function ◽  
Sequence Data ◽  
Weight Coefficient ◽  
Cluster Center ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine

Since SVM is sensitive to noises and outliers of system call sequence data. A new fuzzy support vector machine algorithm based on SVDD is presented in this paper. In our algorithm, the noises and outliers are identified by a hypersphere with minimum volume while containing the maximum of the samples. The definition of fuzzy membership is considered by not only the relation between a sample and hyperplane, but also relation between samples. For each sample inside the hypersphere, the fuzzy membership function is a linear function of the distance between the sample and the hyperplane. The greater the distance, the greater the weight coefficient. For each sample outside the hypersphere, the membership function is an exponential function of the distance between the sample and the hyperplane. The greater the distance, the smaller the weight coefficient. Compared with the traditional fuzzy membership definition based on the relation between a sample and its cluster center, our method effectively distinguishes the noises or outlies from support vectors and assigns them appropriate weight coefficients even though they are distributed on the boundary between the positive and the negative classes. The experiments show that the fuzzy support vector proposed in this paper is more robust than the support vector machine and fuzzy support vector machines based on the distance of a sample and its cluster center.

Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine

2017 ◽  
Vol 16 (2) ◽  
pp. 116-121 ◽  
Author(s):  
Shuihua Wang ◽  
Yang Li ◽  
Ying Shao ◽  
Carlo Cattani ◽  
Yudong Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Dendritic Spines ◽  
Wavelet Packet ◽  
Support Vector ◽  
Fuzzy Support Vector Machine
Sign in / Sign up

Export Citation Format

Share Document