Proceedings Article10.1145/2908446.2908460
Ensemble Based-Feature Selection on Human Activity Recognition
Hussein Mazaar,Eid Emary,Hoda M. Onsi +2 more
- 09 May 2016
- pp 81-87
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TL;DR: An approach for ensemble-based feature selection in human activity recognition is presented to select an important discriminating features to recognize the human activities in videos and removing the irrelevant redundant features.
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Abstract: The paper presents an approach for ensemble-based feature selection in human activity recognition. The goal is to select an important discriminating features to recognize the human activities in videos and removing the irrelevant redundant features. The features are extracted based on spatiotemporal orientation energy and template matching. Due to robust and accurate ensemble models with low variability and biases, Gradient Boosting and Random Forest are applied to identify the relevant features. Support Vector Machine with linear kernel is used to classify the activities. The experiments have tested on KTH dataset. The results show an improvement in accuracy (better by 1.51%) and the features are reduced by 99.2%. The Comparisons to related works were given.
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Citations
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Henry Friday Nweke,Henry Friday Nweke,Ying Wah Teh,Ghulam Mujtaba,Ghulam Mujtaba,Mohammed Ali Al-Garadi +5 more
TL;DR: The focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices.
389
Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach
TL;DR: Kalman filter appear to be the more efficient method, since it exhibited both good accuracy and short processing time and properties that play a large role in real-time applications using wearable devices.
72
A correlation-based binary particle swarm optimization method for feature selection in human activity recognition:
TL;DR: A correlation-based binary particle swarm optimization method for feature selection in human activity recognition, which can work well with six classifiers, and can improve the classification accuracy in the OPPORTUNITY Activity Recognition dataset.
21
Feature Selection with Dynamic Classifier Ensembles
Hakan Ezgi Kiziloz,Ayça Deniz +1 more
- 11 Oct 2020
TL;DR: A new multiobjective selection model that dynamically searches for the best ensemble of five classifiers to extract the best representative feature subsets is proposed and shows that the proposed method performs significantly better than all the machine learning techniques when they are executed separately.
3
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