scholarly journals Klasifikasi Sinyal Elektrokardiogram Menggunakan Renyi Entropy

2018 ◽  
Vol 4 (2) ◽  
pp. 11-18
Author(s):  
Nano Estananto
Keyword(s):  
Support Vector Machine ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Support Vector ◽  
Signal Complexity

Sinyal Elektrokardiogram (EKG) adalah salah satu indikator penting tentang aktivitas kelistrikan jantung. Sinyal ini dapat menjadi basis analisa kerja jantung sehingga menjadi penting. Sampai saat ini sinyal EKG tidak dapat digambarkan dengan persamaan matematis yang tepat karena sinyal EKG berulang tapi tidak tepat periodik. Untuk itu digunakan signal complexity untuk menjelaskan perilaku sinyal EKG. Renyl entropy adalah salah satu matrik untuk menganalisa kompleksitas sinyal, dan merupakan bentuk umum dari entropy lain. Dengan demikian akurasi yang dihasilkan lebih tinggi. Pada penelitian ini menggunakan Renyi Entropy dihasilkan akurasi 100% untuk tiga kelas data EKG. Digunakan Support Vector Machine (SVM) linear, qubic, dan quadratic sebagai pembanding satu sama lain. Keunggulan metode ini adalah akurasi yang tinggi dengan menggunakan jumlah ciri yang jauh lebih sedikit.

Prediction Model of Least Squares Support Vector Machine of Increased Memory Type Based on GA and Quadratic Renyi-Entropy

2011 ◽  
Vol 219-220 ◽  
pp. 754-761
Author(s):  
Guan Hua Zhao ◽  
Wen Wen Yan
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Financial Distress ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Support Vector ◽  
Memory Type

In order to improve the accuracy of financial achievement, this paper applies a new forecast model of the Increased memory type least squares support vector machine base on neighborhood rough set and quadratic Renyi-entropy on the basis of the traditional support vector machine prediction model. The paper also independently derives the entropy fit for the financial distress prediction which is in discrete sequence, as well as the expression of support vector machine kernel function. The experimental results show that the improved model is significantly superior to the traditional LS-SVM as well as the standard support vector machine prediction model, regardless of the forecast accuracy , training samples number.

Product Recommendation System Using Tunicate Swarm Magnetic Optimization Algorithm-Based Black Hole Renyi Entropy Fuzzy Clustering and K-Nearest Neighbour

2021 ◽  
pp. 2150033
Author(s):  
Shefali Gupta ◽  
Meenu Dave
Keyword(s):  
Black Hole ◽  
Fuzzy Clustering ◽  
Recommendation System ◽  
User Preferences ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Product Recommendation ◽  
Optimisation Algorithm

The recommendation system takes the information related to the user profile or interest to suggest the user with convenient materials that the user is interested in. Most of the existing implicit methods find the user preferences and automatically recommend the desired products in the interface, but failed to generate user-oriented results. Hence, an effective product recommendation method is developed in this research using the proposed Tunicate Swarm Magnetic Optimisation Algorithm-based Black Hole Renyi Entropy Fuzzy Clustering+K-Nearest Neighbour (TSMOA-based BHrEFC+KNN) to generate more user convenient result by grouping relevant products and recommends the similar products to users with great interest. The proposed TSMOA is designed by integrating the Tunicate Swarm Algorithm (TSA) and Magnetic Optimisation Algorithm (MOA), respectively. With the entropy measure and Jaro–Winkler distance, the process of group matching and the matching sequence of visitor and query are performed more effectively that enable to achieve the sentiment classification based on the binary visitor sequence. The performance obtained by the proposed TSMOA-based BHrEFC+KNN is evaluated in terms of accuracy, True Positive Rate (TPR), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the proposed system is compared with the existing methods, such as Deep learning+Naïve–Bayes, K-medoids clustering+Long Short-Term Memory (LSTM), BHEFC+Support Vector Machine (SVM) and TSMOA-Black Hole Entropy Fuzzy Clustering (BHEFC)[Formula: see text]Neural Network (NN), in which the TSMOA-BHEFC+NN obtained better results. The proposed TSMOA-based BHrEFC+KNN is 0.32%, 0.29%, 50%, and 6.44% is better than the existing TSMOA-BHEFC+NN in terms of accuracy, TPR, MAPE, and RMSE, respectively.

Prediction Model of Non-Iterative Least Squares Support Vector Machines Based on Quadratic Renyi-Entropy

2011 ◽  
Vol 474-476 ◽  
pp. 967-972
Author(s):  
Guan Hua Zhao
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Financial Distress ◽  
Computing Time ◽  
Forecast Accuracy ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Support Vector ◽  
Svm Model ◽  
Vector Machines

By comparing and analysing the model of non-iterative least squares support vector machines (LS-SVM) based on quadratic Renyi-entropy, traditional LS-SVM model and standard support vector machines (SVM) model, this paper concludes whether the number of training samples or computing time,non-iterative LS-SVM model based on quadratic Renyi-entropy are significantly better than the model of traditional LS-SVM and standard SVM model and it also proves the effectiveness of applying the concept of quadratic Renyi-entropy on financial distress prediction. At the same time, by the comparison of different point of 3 years of ST which is from 1to 2, the author concludes the forecast accuracy of 1 year ago before ST, the further distance away from the piont of ST, the lower the prediction accuracy is.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

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.

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