scholarly journals Analisis Optimasi Algoritma Klasifikasi Support Vector Machine, Decision Trees, dan Neural Network Menggunakan Adaboost dan Bagging

2020 ◽  
Vol 5 (3) ◽  
pp. 247
Author(s):  
Agus Heri Yunial
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Support Vector Machine ◽  
Decision Trees ◽  
Classification Algorithm ◽  
Test Method ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
The Neural Network ◽  
Vector Machines

The accuracy value of a classification algorithm shows whether the algorithm is good or not in classifying data which can affect the results of the classification method in data mining processing. In this study, the author will analyze the effect of optimization using the adaboost and bagging methods on the results of the classification algorithm accuracy value on support vector machines, decision trees, and neural networks. This study uses a software in data mining processing that is using the Weka application version 3.8.1. The test method used was a percentage split of 70%. In this study, the results show that adaboost optimization can increase the accuracy value of the support vector machine algorithm from 88.93% to 89.10%, decision trees from 90.24% to 90.36%, and neural network from 88.53% to 88.61%, while bagging optimization can only increase Algortima decision trees become 90.55%, and the neural network becomes 90.38%, because the accuracy value of the support vector machine algorithm is the same as the accuracy value of bagging, which is 88.93%.

Study of Fuzzy Integral and Support Vector Machine Algorithm in Machinery Diagnosis

2006 ◽  
Vol 532-533 ◽  
pp. 496-499
Author(s):  
Wangs Shen Hao ◽  
Xun Sheng Zhu ◽  
Jian Cai Zhao ◽  
Biao Jun Tian
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Support Vector ◽  
Fuzzy Integral ◽  
Support Vector Machine Algorithm ◽  
The Neural Network ◽  
Vector Machines ◽  
Diagnosis Accuracy

In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. To improve the diagnosis accuracy a method that combines the multi-class support vector machines (MSVMs) outputs with the degree of importance of individual MSVMs based on fuzzy integral is presented. This provides a sound basis for integrating the results from MSVMs to get more accurate classification. The experimental results with the recognition problem of a blower machine show the performance of fault diagnosis can be improved.

DMiner-I: A software tool of data mining and its applications

Robotica ◽  
2002 ◽  
Vol 20 (5) ◽  
pp. 499-508
Author(s):  
Jie Yang ◽  
Chenzhou Ye ◽  
Nianyi Chen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Knowledge Representation ◽  
Decision Trees ◽  
Software Tool ◽  
Support Vector ◽  
Function Models

SummaryA software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), and computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine and visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized byVisual C++under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

scholarly journals Design and implementation of an intelligent vision and sorting system

2009 ◽  
Author(s):  
◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Support Vector Machines ◽  
Robot Control ◽  
Support Vector ◽  
Design And Implementation ◽  
The Neural Network ◽  
Vector Machines ◽  
Control Computer ◽  
Sorting System

This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

scholarly journals An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

2019 ◽  
Vol 10 (1) ◽  
pp. 47-54
Author(s):  
Abdullah Jafari Chashmi ◽  
Mehdi Chehel Amirani
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Heart Diseases ◽  
Selection Procedure ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Machine Method ◽  
The Neural Network ◽  
Ecg Arrhythmia

Abstract Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.

scholarly journals The Data Mining Support Vector Machine Algorithm used for Detecting and Forecasting of Crimes

2019 ◽  
Vol 9 (1) ◽  
pp. 395-401
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Mining System ◽  
Data Mining Techniques ◽  
Crime Data ◽  
Data Archives ◽  
Crime Investigation ◽  
Chicago Police

Crime rate is expanding extremely more because of destitution and joblessness. With the current crime investigation techniques, officers need to invest a great deal of energy just as labor to recognize suspects and criminals. Anyway crime investigation procedure should be quicker and dynamic. As huge amount of data is gathered during crime investigation, data mining is a methodology which can be valuable in this viewpoint. Data mining is a procedure that concentrates valuable data from enormous amount of crime data with the goal that potential suspects of the crime can be recognized productively. Quantities of data mining techniques are accessible. Utilization of specific data mining system has more prominent impact on the outcomes acquired. So the exhibition of three data mining techniques will be analyzed against test crime and criminal database and best performing algorithm will be utilized against test crime and criminal database to recognize potential suspects of the crime. Data mining is a procedure of separating information from colossal amount of data put away in databases, data stockrooms and data archives. Clustering is the way toward consolidating data objects into gatherings. Here taken the Crime dataset from Chicago police website and implemented in MATLAB utilizing Support Vector Machine algorithm.

scholarly journals Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara

2020 ◽  
Vol 7 (1) ◽  
pp. 53
Author(s):  
Derisma Derisma ◽  
Fajri Febrian
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Accuracy Rate ◽  
Cancer Disease ◽  
Network Support

Abstrak: Kanker payudara merupakan jenis kanker yang sering ditemukan oleh kebanyakan wanita. Di Indonesia Kanker payudara menempati urutan pertama pada pasien rawat inap di seluruh rumah sakit. Tujuan dari penelitian ini adalah melakukan diagnosis penyakit kanker payudara berbasis komputasi yang dapat menghasilkan bagaimana kondisi kanker seseorang berdasarkan akurasi algoritma. Penelitian ini menggunakan pemrograman orange python dan dataset Wisconsin Breast Cancer untuk pemodelan klasifikasi kanker payudara. Metode data mining yang diterapkan yaitu Neural Network, Support Vector Machine, dan Naive Bayes. Dalam penelitian ini didapat algoritma klasifikasi terbaik yaitu algoritma Kernel SVM dengan tingkat akurasi sebesar  98.9 % dan algoritma terendah yaitu Naive Bayes senilai 96.1 %.   Kata kunci: kanker payudara, neural network, support vector machine, naive bayes   Abstract: Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python orange programming and Wisconsin Breast Cancer dataset for a modeling and application of breast cancer classification. The data mining methods that were applied in this study were Neural Network, Support Vector Machine, dan Naive Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 98.9 % accuracy rate and Naïve Beyes was the lowest with 96.1 % of accuracy rate.   Keywords: breast cancer, neural network, support vector machine, naive bayes

Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kavitha Senthil ◽  
Vidyaathulasiramam
Keyword(s):  
Neural Network ◽  
Ovarian Cancer ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Cancer Diagnosis ◽  
Support Vector ◽  
Hybrid Neural Network ◽  
Cancer Prediction ◽  
The Neural Network

Abstract Objectives This paper proposed the neural network-based segmentation model using Pre-trained Mask Convolutional Neural Network (CNN) with VGG-19 architecture. Since ovarian is very tiny tissue, it needs to be segmented with higher accuracy from the annotated image of ovary images collected in dataset. This model is proposed to predict and suppress the illness early and to correctly diagnose it, helping the doctor save the patient's life. Methods The paper uses the neural network based segmentation using Pre-trained Mask CNN integrated with VGG-19 NN architecture for CNN to enhance the ovarian cancer prediction and diagnosis. Results Proposed segmentation using hybrid neural network of CNN will provide higher accuracy when compared with logistic regression, Gaussian naïve Bayes, and random Forest and Support Vector Machine (SVM) classifiers.

The Accuracy Comparison of Assessing the Amount of Steel Corrosion in Concrete with Several Different Algorithms

2012 ◽  
Vol 166-169 ◽  
pp. 1958-1962
Author(s):  
Ping Jie Cheng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Sample Size ◽  
Predictive Value ◽  
Steel Corrosion ◽  
Experimental Results ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Accuracy Comparison

Many before studies showed that it was difficult to ensure the accuracy of assessing the amount of steel corrosion in the cracking concrete with artificial neural network [3] method while the study sample size was small. This paper introduces several different algorithms to assess the amount of steel corrosion in concrete. The experimental results show that compared with other algorithms, the predictive value of the support vector machine algorithm is the closest to the measured value.

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