scholarly journals Feature Selection for Work Recognition and Working Motion Measurement

2018 ◽  
Vol 30 (5) ◽  
pp. 706-716 ◽  
Author(s):  
Saori Miyajima ◽  
Takayuki Tanaka ◽  
Natsuki Miyata ◽  
Mitsunori Tada ◽  
Masaaki Mochimaru ◽  
...  
Keyword(s):  
Feature Selection ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Feature Selection Method ◽  
The Body ◽  
Support Vector ◽  
Body Parts ◽  
Care Services ◽  
Physical Burden ◽  
New Feature

As the demand for nursing care services is growing, the physical burden involved in caregiving has drawn widespread attention. To mitigate the physical burden in caregiving, we have to recognize what kind of work and problems are involved in each caregiving task. To identify the problems involved in caregiving, we need to recognize the work and analyze its workload. Aiming to reduce the burden on the waist during caregiving tasks, we are developing inertial sensor suits for measuring the working motions. With the developed method, the burden on the waist is estimated from the waist posture. Considering its use in practical caregiving sites, the number of inertial sensors should be the minimum necessary, which depends on the number of body parts where to measure the posture. In this study, we select the body parts to achieve the two above-mentioned goals: to recognize the work involved in caregiving and capture the waist posture. A support vector machine (SVM) is used to recognize the work. Its conventional method of selecting the features on which to recognize the work only considers the recognition accuracy and does not sufficiently meet the needs for measuring the postures. Therefore, we propose a new feature-selection method, which can evaluate the waist-posture measuring accuracy and can make forward feature selections in the same manner as the conventional wrapper method. We have verified the effectiveness of the proposed method by measuring simple simulated work motions.

Discriminative Feature Selection Based on Imbalance SVDD for Fault Detection of Semiconductor Manufacturing Processes

2016 ◽  
Vol 25 (11) ◽  
pp. 1650143 ◽  
Author(s):  
Jian Wang ◽  
Jian Feng ◽  
Zhiyan Han
Keyword(s):  
Feature Selection ◽  
Fault Detection ◽  
Semiconductor Industry ◽  
Feature Selection Method ◽  
Class Imbalance ◽  
Support Vector ◽  
Support Vector Data Description ◽  
The Real ◽  
Discriminative Feature ◽  
New Feature

Feature selection has become a key step of fault detection. Unfortunately, the class imbalance in the modern semiconductor industry makes feature selection quite challenging. This paper analyzes the challenges and indicates the limitations of the traditional supervised and unsupervised feature selection methods. To cope with the limitations, a new feature selection method named imbalanced support vector data description-radius-recursive feature selection (ISVDD-radius-RFE) is proposed. When selecting features, the ISVDD-radius-RFE has three advantages: (1) ISVDD-radius-RFE is designed to find the most representative feature by finding the real shape of normal samples. (2) ISVDD-radius-RFE can represent the real shape of normal samples more correctly by introducing the discriminant information from fault samples. (3) ISVDD-radius-RFE is optimized for fault detection where the imbalance data is common. The kernel ISVDD-radius-RFE is also described in this paper. The proposed method is demonstrated through its application in the banana set and SECOM dataset. The experimental results confirm ISVDD-radius-RFE and kernel ISVDD-radius-RFE improve the performance of fault detection.

scholarly journals E-mail spam filtering by a new hybrid feature selection method using IG and CNB wrapper

2013 ◽  
Vol 2 (3) ◽  
Author(s):  
Seyed Mostafa Pourhashemi
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Feature Selection Method ◽  
Text Messages ◽  
The Body ◽  
Support Vector ◽  
Svm Classifier ◽  
Hybrid Architecture ◽  
Ve Bayes ◽  
Optimal Accuracy

The growing volume of spam emails has resulted in the necessity for more accurate and efficient email classification system. The purpose of this research is presenting an machine learning approach for enhancing the accuracy of automatic spam detecting and filtering and separating them from legitimate messages. In this regard, for reducing the error rate and increasing the efficiency, the hybrid architecture on feature selection has been used. Features used in these systems, are the body of text messages. Proposed system of this research has used the combination of two filtering models, Filter and Wrapper, with Information Gain (IG) filter and Complement Naïve Bayes (CNB) wrapper as feature selectors. In addition, Multinomial Naïve Bayes (MNB) classifier, Discriminative Multinomial Naïve Bayes (DMNB) classifier, Support Vector Machine (SVM) classifier and Random Forest classifier are used for classification. Finally, the output results of this classifiers and feature selection methods are examined and the best design is selected and it is compared with another similar works by considering different parameters. The optimal accuracy of the proposed system is evaluated equal to 99%.

scholarly journals Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Congwei Sun ◽  
Zhijun Dai ◽  
Hongyan Zhang ◽  
Lanzhi Li ◽  
Zheming Yuan
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Principal Component ◽  
Neuronal Morphology ◽  
Support Vector ◽  
Neuron Type ◽  
Neuronal Function ◽  
Binary Matrix ◽  
New Feature ◽  
Feature Dimension

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

A lazy feature selection method for multi-label classification

2021 ◽  
Vol 25 (1) ◽  
pp. 21-34
Author(s):  
Rafael B. Pereira ◽  
Alexandre Plastino ◽  
Bianca Zadrozny ◽  
Luiz H.C. Merschmann
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Selection Method ◽  
Video Classification ◽  
Classification Problems ◽  
Class Label ◽  
New Feature ◽  
Feature Selection Techniques ◽  
Biomolecular Analysis

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

A New Feature Selection Method for Enhancing Cancer Diagnosis Based on DNA Microarray

2020 ◽  
Author(s):  
Mostafa Atlam ◽  
Hanaa Torkey ◽  
Hanaa Salem ◽  
Nawal El-Fishawy
Keyword(s):  
Feature Selection ◽  
Dna Microarray ◽  
Cancer Diagnosis ◽  
Feature Selection Method ◽  
Selection Method ◽  
New Feature
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