An efficient feature selection method for mobile devices with application to activity recognition

Authors:
Jian-Xun Peng;Stuart Ferguson;Karen Rafferty;Paul D. Kelly
Affiliations:
The School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Ashby Building, Stranmillis Road, Belfast BT9 5AH, UK;The School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Ashby Building, Stranmillis Road, Belfast BT9 5AH, UK;The School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Ashby Building, Stranmillis Road, Belfast BT9 5AH, UK;The School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Ashby Building, Stranmillis Road, Belfast BT9 5AH, UK
Venue:
Neurocomputing
Year:
2011

Citing 28
Cited 3

Integer and combinatorial optimization

Integer and combinatorial optimization
Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Iterative fast orthogonal search algorithm for MDL-based training of generalized single-layer networks

Neural Networks
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Activity and Location Recognition Using Wearable Sensors

IEEE Pervasive Computing
Feature Subset Selection Using a Genetic Algorithm

IEEE Intelligent Systems
Feature Selection via Concave Minimization and Support Vector Machines

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Towards a Better Understanding of Context and Context-Awareness

HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Unsupervised Clustering of Symbol Strings and Context Recognition

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
What Shall We Teach Our Pants?

ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
An introduction to variable and feature selection

The Journal of Machine Learning Research
Bayesian approach to sensor-based context awareness

Personal and Ubiquitous Computing
Introduction

Communications of the ACM - The disappearing computer
Context is key

Communications of the ACM - The disappearing computer
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Capturing human motion using body-fixed sensors: outdoor measurement and clinical applications: Research Articles

Computer Animation and Virtual Worlds
Multi-class feature selection for texture classification

Pattern Recognition Letters
FS_SFS: A novel feature selection method for support vector machines

Pattern Recognition
Feature selection based on rough sets and particle swarm optimization

Pattern Recognition Letters
Neural input selection-A fast model-based approach

Neurocomputing
Multiclass SVM-RFE for product form feature selection

Expert Systems with Applications: An International Journal
A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications

Human-Computer Interaction
A wrapper method for feature selection using Support Vector Machines

Information Sciences: an International Journal
Feature subset selection in large dimensionality domains

Pattern Recognition
Machine learning techniques and chi-square feature selection for cancer classification using SAGE gene expression profiles

BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Linear penalization support vector machines for feature selection

PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Farther than you may think: an empirical investigation of the proximity of users to their mobile phones

UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
A two-stage algorithm for identification of nonlinear dynamic systems

Automatica (Journal of IFAC)

Integrating a differential evolution feature weighting scheme into prototype generation

Neurocomputing
A sequential algorithm for sparse support vector classifiers

Pattern Recognition
Mobile context inference using two-layered Bayesian networks for smartphones

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modeled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain-based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.