scholarly journals Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1247
Author(s):  
Mingyang Liu ◽  
Jin Yang ◽  
Wei Zheng
Keyword(s):  
Support Vector Machine ◽  
Maximum Entropy ◽  
Leak Detection ◽  
Least Square ◽  
Support Vector ◽  
Maxent Model ◽  
Water Pipelines ◽  
The Impact ◽  
Multi Classification ◽  
Improved Support Vector Machine

Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM.

scholarly journals Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Kun Zhang ◽  
Minrui Fei ◽  
Xin Li ◽  
Huiyu Zhou
Keyword(s):  
Support Vector Machine ◽  
Mean Shift ◽  
Least Square ◽  
Support Vector ◽  
Svm Classifier ◽  
Least Square Estimation ◽  
Unstructured Environments ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

A Research of GPS Height Fitting in Mountainous Terrain by CPSO Optimization FLS-SVM

2013 ◽  
Vol 336-338 ◽  
pp. 2339-2343 ◽  
Author(s):  
Yong Gan ◽  
Xin Xin Liu ◽  
Yuan Pan Zheng
Keyword(s):  
Support Vector Machine ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Mountainous Area ◽  
Machine Model ◽  
Normal Height ◽  
Fitting Method ◽  
Fitting In ◽  
The Impact

For the problem of data limited in the mountainous area, a method of FLS-SVM (Fuzzy Least Square Vector Machine) that supporting small sample data and having high noise ability was put forward. The CPSO(chaos particle swarm optimization algorithm) is adopted to optimize the parameters of least squares support vector machine algorithm, and to avoid the uncertainty of artificial parameter selection. Meanwhile, considering the impact of terrain, the terrain correction is introduced to the support vector machine model. The experimental results show that the model can get higher precision fitting effect compared with traditional fitting method such as PSO-LSSVM and GA-LSSVM, and suitable for the SRTM application of getting normal height.

scholarly journals IDENTIFIKASI KEBUTUHAN DASAR DI TEMPAT EVAKUASI SEMENTARA PASCA ERUPSI MERAPI DENGAN SENTIMENT ANALISIS DAN SUPPORT VECTOR MACHINE

Telematika ◽  
2018 ◽  
Vol 15 (1) ◽  
pp. 77
Author(s):  
Resky Rayvano Moningka ◽  
Djoko Budiyanto Setyohadi ◽  
Khaerunnisa Khaerunnisa ◽  
Pranowo Pranowo
Keyword(s):  
Support Vector Machine ◽  
Public Opinion ◽  
Maximum Entropy ◽  
Disaster Management ◽  
Cross Validation ◽  
Basic Needs ◽  
Classification Model ◽  
Support Vector ◽  
Twitter Data ◽  
The Impact

AbstractMount Merapi Eruption in 2010 was the biggest after 1872. The impact of this eruption was felt by people who lived around the areas which were affected by this Merapi Eruption. Thus, disaster management was done. One of the disaster management was the fulfillment of basic needs. This research aims to collect public opinion against the fulfillment of basic needs in the shelters after Merapi Eruption based on Twitter data. The algorithm which is used in this research is Support Vector Machine to develop classification model over the data that has been collected. The expected result from this study is to know the basic needs in a shelter. The accuracy gained by performing Cross Validation for 10 folds from Support Vector Machine is 87.96% and Maximum Entropy is 87.45%. Keywords: twitter, sentiment analisis, merapi eruption, support vector machine AbstrakErupsi Gunung Merapi 2010 merupakan yang terbesar setelah tahun 1872. Dampak dari Erupsi Gunung Merapi dirasakan oleh masyarakat yang tinggal di daerah terdampak Erupsi Merapi. Oleh sebab itu dilakukan penanggulangan Bencana. salah satu penanggulangan bencana adalah pemenuhan kebutuhan dasar. Penelitian ini bertujuan untuk mengumpulkan opini publik terhadap pemenuhan kebutuhan dasar di tempat pengungsian pasca erupsi merapi berdasarkan data Twitter. Algoritma yang digunakan dalam penelitian ini adalah Support Vector Machine untuk membangun model klasifikasi atas data yang sudah dikumpulkan.   Hasil yang diharapkan dari penelitian ini adalah mengetahui kebutuhan dasar dari suatu tempat pengungsian. Akurasi yang didapatkan dengan melakukan Cross Validation sebanyak 10 fold dari model klasifikasi Support Vector Machine87,96% dan Maximum Entropy 87,45 Kata Kunci: twitter, analisis sentimen, erupsi merapi, support vector machine

Histogram Based Bacteria Colony Features Analysis

2014 ◽  
Vol 609-610 ◽  
pp. 1448-1452
Author(s):  
Kun Zhang ◽  
Min Rui Fei
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Least Square ◽  
Support Vector ◽  
Least Square Estimation ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. This paper presents a novel approach for adaptive colony segmentation by classifying the detected peaks of intensity histograms of images. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained support vector machine (USVM) has better recognition accuracy than the other state of the art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

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