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.

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%.

scholarly journals Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

2009 ◽  
Vol 27 (No. 6) ◽  
pp. 393-402 ◽  
Author(s):  
H. Lin ◽  
J. Zhao ◽  
Q. Chen ◽  
J. Cai ◽  
P. Zhou
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Crack Detection ◽  
Support Vector Machine Model ◽  
Acoustic Resonance ◽  
Support Vector ◽  
Machine Model ◽  
Nearest Neighbours

A system based on acoustic resonance was developed for eggshell crack detection. It was achieved by the analysis of the measured frequency response of eggshell excited with a light mechanism. The response signal was processed by recursive least squares adaptive filter, which resulted in the signal-to-noise ratio of the acoustic impulse response reing remarkably enhanced. Five features variables were exacted from the response frequency signals. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were examined comparatively in this work. Some parameters of the model were optimised by cross-validation in the building model. The experimental results showed that the performance of the support vector machine model is the best in comparison to k-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with the identification rates of 95.1% in the calibration set, and 97.1% in the prediction set, respectively. Based on the results, it was concluded that the acoustic resonance system combined with the supervised pattern recognition has a significant potential for the cracked eggs detection.

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

scholarly journals Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242899
Author(s):  
Musatafa Abbas Abbood Albadr ◽  
Sabrina Tiun ◽  
Masri Ayob ◽  
Fahad Taha AL-Dhief ◽  
Khairuddin Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Ct Scan ◽  
Extreme Learning Machine ◽  
Experimental Result ◽  
Support Vector ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

scholarly journals Traffic travel pattern recognition based on sparse Global Positioning System trajectory data

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096846
Author(s):  
Juan Chen ◽  
Kepei Qi ◽  
Shiyu Zhu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Convolutional Neural Network ◽  
Global Positioning System ◽  
Support Vector ◽  
Positioning System ◽  
Travel Pattern ◽  
Global Positioning

This article mainly uses sparse Global Positioning System trajectory data to identify traffic travel pattern. In this article, the data are preprocessed and the eigenvalues are calculated. Then, the Global Positioning System track points are identified and extracted by walking and non-walking segments. Finally, the three machine learning models of support-vector machine, decision tree, and convolutional neural network are used for comparison experiments. The innovation of this article is to propose a walking and non-walking identification method based on density-based spatial clustering of applications with noise clustering. The method takes into account the continuous state between the geographical distributions, and it has better noise immunity against the influence of external factors. In this process, this article directly achieves better conversion point recognition results through the Global Positioning System track point information, which lays a good foundation for the accuracy of travel pattern recognition. The experimental results of this article show that compared with threshold-based and multi-layer perceptron–based methods, the recognition method based on density-based spatial clustering of applications with noise clustering has the highest accuracy, reaching 82.20%. Then, support-vector machine, decision tree, and convolutional neural network are used for traffic travel pattern recognition. The F1-score of support-vector machine is the highest, reaching 0.84, and the F1-scores of decision tree and convolutional neural network are 0.78 and 0.80, respectively. Finally, the support-vector machine was used for the recognition test to achieve an accuracy of 76.83%.

scholarly journals Simple quantum circuit for pattern recognition based on nearest mean classifier

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Gharib M Subhi ◽  
Azeddine Messikh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Quantum Circuit ◽  
Support Vector ◽  
Bloch Sphere ◽  
Optimal Hyperplane ◽  
Simple Quantum ◽  
Quantum Matrix

Machine learning plays a key role in many applications such as data mining and image recognition.Classification is one subcategory under machine learning. In this paper we propose a simple quantum circuitbased on the nearest mean classifier to classified handwriting characters. Our circuit is a simplified circuit fromthe quantum support vector machine [Phys. Rev. Lett. 114, 140504 (2015)] which uses quantum matrix inversealgorithm to find optimal hyperplane that separated two different classes. In our case the hyperplane is foundusing projections and rotations on the Bloch sphere.

scholarly journals A Review Study on Data Mining Algorithms for Prediction Diseases

2020 ◽  
pp. 504-507
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector ◽  
Healthcare Industry ◽  
Network Support ◽  
Data Mining Algorithms ◽  
Review Study ◽  
Using Data ◽  
Mining Algorithms

The healthcare industry assembles massive volume of healthcare information or data that circulate the information into useful data. In everyday life several factors that affect the human diseases. Hospitals are producing large amount of information related to patients. This paper describes the various data mining algorithms such as neural network, support vector machine, KNN, decision tree etc. and provides an overall brief of the existing work. The major advantage of using data mining is that to identify the structures.

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