Human motion sequence recognition based on correlation feature selection and multilayer perceptron

2020 ◽  
Author(s):  
Yongjun Gao ◽  
Mei Wang ◽  
Yunlei Yu ◽  
Chen Zhang
Keyword(s):  
Feature Selection ◽  
Multilayer Perceptron ◽  
Human Motion ◽  
Sequence Recognition ◽  
Motion Sequence

scholarly journals Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fei Tan ◽  
Xiaoqing Xie
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithm ◽  
Human Motion ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Recording Device ◽  
Table Tennis ◽  
Movement Recognition ◽  
Random Forest Tree

Human motion recognition based on inertial sensor is a new research direction in the field of pattern recognition. It carries out preprocessing, feature selection, and feature selection by placing inertial sensors on the surface of the human body. Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis. Accurately identifying these movement modes is of great significance for swing movement analysis. With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual sensors. However, there is not much work on movement recognition for table tennis, and the methods are still mainly integrated into the traditional field of machine learning. Therefore, this paper uses an acceleration sensor as a motion recording device for a table tennis disc and explores the three-axis acceleration data of four common swing motions. Traditional machine learning algorithms (decision tree, random forest tree, and support vector) are used to classify the swing motion, and a classification algorithm based on the idea of integration is designed. Experimental results show that the ensemble learning algorithm developed in this paper is better than the traditional machine learning algorithm, and the average recognition accuracy is 91%.

scholarly journals PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yanjuan Li ◽  
Zitong Zhang ◽  
Zhixia Teng ◽  
Xiaoyan Liu
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Prediction Model ◽  
Multilayer Perceptron ◽  
Type Ii Diabetes ◽  
Cross Validation ◽  
Experimental Results ◽  
Type Ii ◽  
Fold Cross Validation ◽  
Better Than

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer’s disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

scholarly journals Feature Selection Correlation-Based pada Prediksi Nasabah Bank Telemarketing untuk Deposito

2021 ◽  
Vol 20 (2) ◽  
pp. 335-342
Author(s):  
Annisa Nurul Puteri ◽  
Arizal Arizal ◽  
Andini Dani Achmad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Selection ◽  
Data Collection ◽  
Multilayer Perceptron ◽  
Customer Data ◽  
Previous Contact ◽  
Banking Institution

Pre-processing merupakan tahap yang penting dalam melakukan klasifikasi data. Pre-processing berguna untuk mempersiapkan data sehingga teknik klasifikasi yang diterapkan menghasilkan pola yang berkualitas dan akurat. Salah satu teknik data pre-processing yang sering digunakan untuk mengetahui atribut yang paling berpengaruh pada sebuah dataset adalah feature selection. Data yang digunakan dalam penelitian ini adalah customer data collection dari a Portuguese banking institution in UCI Machine Learning Repository. Penelitian ini menggunakan metode feature selection correlation-based yang dikombinasikan dengan metode klasifikasi Multilayer Perceptron Neural Networks. Tujuan penelitian ini untuk mengidentifikasi atribut yang paling relevan dan berpengaruh dari dataset dalam memprediksi nasabah yang potensial untuk penawaran deposito berjangka. Penelitian ini menghasilkan 10 atribut yang memiliki ranking teratas. Atribut-atribut tersebut adalah duration, previous, contact, cons.price.idx, month, cons.cof.idx, age, job, marital, dan housing. Hasil klasifikasi dari atribut yang terpilih memiliki tingkat akurasi tertinggi sebesar 80.5% dan tingkat akurasi terendah 79.1%.

scholarly journals Feature Selection using Hybrid Dragonfly Algorithm in a Heart Disease Predication System

2019 ◽  
Vol 8 (6) ◽  
pp. 2862-2867
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Heart Disease ◽  
Mutual Information ◽  
Multilayer Perceptron ◽  
Time Algorithm ◽  
Support Vector ◽  
Ordinary People ◽  
Fatal Disease ◽  
Dragonfly Algorithm

The heart disease considers as one of the fatal disease in many countries. The main reason is due to the approved methods of diagnostic are not available to the ordinary people. Many studies have been done to handle this case with the use of both methods of soft computing and machine learning. In this study, a hybrid binary dragonfly algorithm and mutual information proposed for feature selection, support vector machine and multilayer perceptron employed for classification. The Statlog dataset used for experiments. Out of a total of 270 instances of patient data, 216 employees for the purpose of practicing, 54 of them used for the purpose of examining. Maximum classification accuracy of 94.44% achieved with support vector machine and 92.59% with multilayer perceptron on features selected with binary dragonfly algorithm, whereas with features obtained from mutual information combined with binary dragonfly (MI_BDA) algorithm support vector machine and multilayer perceptron attained an accuracy of 96.29%. The time algorithm takes reduced from 15.4 with binary dragonfly algorithm to 6.95 seconds with MI_BDA.

scholarly journals Comparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure

2021 ◽  
Vol 4 (1) ◽  
pp. 14
Author(s):  
Husna Afanyn Khoirunissa ◽  
Amanda Rizky Widyaningrum ◽  
Annisa Priliya Ayu Maharani
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Feature Selection ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Cross Validation ◽  
Early Stage ◽  
Bank Account ◽  
Credit Score

<p>The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy.</p><strong>Keywords:</strong> bank customer, random forest, logistic regression, multilayer perceptron

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