DF-SVM: a decision forest constructed on artificially enlarged feature space by support vector machine

2012 ◽  
Vol 40 (4) ◽  
pp. 467-494 ◽  
Author(s):  
M. Faisal Zaman ◽  
Hideo Hirose
Keyword(s):  
Support Vector Machine ◽  
Feature Space ◽  
Support Vector ◽  
Decision Forest

Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function

2016 ◽  
Vol 25 (3) ◽  
pp. 417-429
Author(s):  
Chong Wu ◽  
Lu Wang ◽  
Zhe Shi
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Financial Distress ◽  
Classification Accuracy ◽  
Feature Space ◽  
Support Vector ◽  
Input Space ◽  
Financial Distress Prediction ◽  
Support Vectors ◽  
Distress Prediction

AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

scholarly journals Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 667
Author(s):  
Wismaji Sadewo ◽  
Zuherman Rustam ◽  
Hamidah Hamidah ◽  
Alifah Roudhoh Chusmarsyah
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Support Vector Machine ◽  
Early Detection ◽  
Feature Space ◽  
Kernel Functions ◽  
Twin Support Vector Machine ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

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.

Self-Learning Based Centrifugal Compressor Surge Mapping With Computationally Efficient Adaptive Asymmetric Support Vector Machine

2012 ◽  
Vol 134 (5) ◽  
Author(s):  
Xin Wu ◽  
Yaoyu Li
Keyword(s):  
Support Vector Machine ◽  
Feature Space ◽  
Ambient Air ◽  
Low Flow ◽  
Detection Methods ◽  
Support Vector ◽  
Three Dimension ◽  
Computationally Efficient ◽  
Surge Line ◽  
Self Learning

When an air compressor is operated at very low flow rate for a given discharge pressure, surge may occur, resulting in large oscillations in pressure and flow in the compressor. To prevent the damage of the compressor, on account of surge, the control strategy employed is typically to operate it below the surge line (a map of the conditions at which surge begins). Surge line is strongly affected by the ambient air conditions. Previous research has developed to derive data-driven surge maps based on an asymmetric support vector machine (ASVM). The ASVM penalizes the surge case with much greater cost to minimize the possibility of undetected surge. This paper concerns the development of adaptive ASVM based self-learning surge map modeling via the combination with signal processing techniques for surge detection. During the actual operation of a compressor after the ASVM based surge map is obtained with historic data, new surge points can be identified with the surge detection methods such as short-time Fourier transform or wavelet transform. The new surge point can be used to update the surge map. However, with increasing number of surge points, the complexity of support vector machine (SVM) would grow dramatically. In order to keep the surge map SVM at a relatively low dimension, an adaptive SVM modeling algorithm is developed to select the minimum set of necessary support vectors in a three-dimension feature space based on Gaussian curvature to guarantee a desirable classification between surge and nonsurge areas. The proposed method is validated by applying the surge test data obtained from a testbed compressor at a manufacturing plant.

Sound feature space effects on the performance of annoyance evaluation model based on support vector machine

2019 ◽  
Vol 154 ◽  
pp. 99-113 ◽  
Author(s):  
Tianpei Feng ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Ping Zhou ◽  
Hui Guo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Evaluation Model ◽  
Feature Space ◽  
Support Vector ◽  
Model Based ◽  
Space Effects

تطبيق تصنيف بيانات اللوكيميا باستخدام خوارزمية AGA-SVM Applying of Classification Leukemia's Dataset using AGA-SVM Algorithm

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
عمر صابر قاسم ◽  
محمد علي محمد
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Data Classification ◽  
Feature Space ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Features Selection ◽  
Svm Algorithm

تعد مسألة اختيار الميزات (Features selection) الضرورية في عملية تصنيف البيانات (Data Classification) من المسائل ذات الأهمية الكبيرة في تحديد كفاءة التقنية المستخدمة للتصنيف خصوصا عندما يكون حجم هذه البيانات كبيرا جدا مثل بيانات اللوكيميا (leukemia) المعتمدة على الجينات. اذ تم استخدام خوارزمية مقترحة(AGA_SVM) مهجنة بين الخوارزمية الجينية المعدلة (Adaptive Genetic Algorithm) مع تقنية الة المتجه الداعم (Support Vector Machine)، اذ تقوم الخوارزمية الجينية المعدلة بتحويل البيانات من فضاء الأنماط العالي البعد (High-D Patterns Space) إلى فضاء الخواص الواطئ (Low-D Feature Space) لأجل تحديد الميزات الضرورية واللازمة لعملية التصنيف والتي تتم من خلال تقنية الة المتجه الداعم. وتبين من خلال التطبيق على بيانات اللوكيميا ان نسبة التصنيف كانت (100%) لحالات التدريب والاختبار بالنسبة للطريقة المقترحة (AGA_SVM) مقارنة مع الطريقة الاعتيادية التي أخطأت في عدة حالات تصنيف، مما يدل على كفاءة الطريقة المقترحة مقارنة مع الطريقة الاعتيادية.

Vibration Gear Fault Diagnostics Technique Using Wavelet Support Vector Machine

2014 ◽  
Vol 564 ◽  
pp. 182-188
Author(s):  
Achmad Widodo ◽  
D.P. Dewi Widowati ◽  
D. Satrijo ◽  
I. Haryanto
Keyword(s):  
Support Vector Machine ◽  
Input Data ◽  
Feature Space ◽  
Testing Procedure ◽  
Haar Wavelet ◽  
Support Vector ◽  
Fault Diagnostics ◽  
Test Rig ◽  
Vibration Data ◽  
Gear Fault

Intelligent diagnostics tool for detecting damaged bevel gears was developed based on wavelet support vector machine (WSVM). In this technique, the existing method of SVM was modified by introducing Haar wavelet function as kernel for mapping input data into feature space. The developed method was experimentally evaluated by vibration data measured from test rig machinery fault simulator (MFS). There were four conditions of gears namely normal, worn, teeth defect and one missing-teeth which has been experimented. Statistical features were then calculated from vibration signals and they were employed as input data for training WSVM. Fault diagnostics of bevel gear was performed by executing classification task in trained WSVM. The accuracy of fault diagnostics were evaluated by testing procedure through vibration data acquired from test rig. The results show that the proposed system gives plausible performance in fault diagnostics based on experimental work.

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