scholarly journals Rough Support Vector Machine for Classification with Interval and Incomplete Data

2020 ◽  
Vol 10 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Robert K. Nowicki ◽  
Konrad Grzanek ◽  
Yoichi Hayashi
Keyword(s):  
Support Vector Machine ◽  
Rough Sets ◽  
Incomplete Data ◽  
Input Data ◽  
Missing Values ◽  
Classification Scheme ◽  
Equivalence Classes ◽  
Support Vector ◽  
Positive Boundary

AbstractThe paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.

Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine

2014 ◽  
Vol 493 ◽  
pp. 337-342 ◽  
Author(s):  
Achmad Widodo ◽  
I. Haryanto ◽  
T. Prahasto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Intelligent System ◽  
Feature Space ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Standard Data ◽  
Rolling Element

This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.

scholarly journals Selection of Specialization Class Using Support Vector Machine (SVM) Method in Sekolah Menengah Atas Negeri 1 Ambon

CAUCHY ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 162-168
Author(s):  
Stevanny Tamaela ◽  
Yopi Andry Lesnussa ◽  
Venn Yan Ishak Ilwaru
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
School Level ◽  
Support Vector ◽  
Student Selection ◽  
Accuracy Rate ◽  
High School Level ◽  
Decision Making System ◽  
Selection Of

The curriculum is a plan to form the abilities and character of children based on a standard. One of its form is the division of specialization classes at the high school level. The 2013 curriculum emphasizes that all students in Indonesia can practice their abilities based on their interests and talents, so students no longer choose majors but choose abilities (interests) in them specialize. This research uses the Support Vector Machine (SVM) method in specialization Decision Making System (DMS) at SMA Negeri 1 Ambon. By using the motivating acceptance factors and student selection as input data, this SVM method that processed with MATLAB Software produces a Classification of Interest Class with an accuracy rate more than 95%.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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