標題: | Associative Classification for Human Activity Inference on Smart Phones |
作者: | Peng, Yu-Hsiang Njoo, Gunarto Sindoro Li, Shou-Chun Peng, Wen-Chih 資訊工程學系 Department of Computer Science |
關鍵字: | Activity recognition;Smart phones;Classification;Associative rule;Discretization;Feature selection |
公開日期: | 1-一月-2014 |
摘要: | With the population of smart phones, the general trend of human activity inference is prospering under a powerful computation capabilities on modern phones. Such an assistant make users life more convenient and help them prevent from unnecessary interferences. In conventional research, the activity inference problem is considered a classification instance, so in this paper we propose an association-based classifier framework (ACF) that aims at exploring the correlation among collected sensor data. Each data consists of multiple sensor readings with a label, e.g., dining, shopping, working, driving, sporting, and entertaining. Note that ACF caters to the discrete data; as a consequence, the continuous sensor readings are needed to be transformed to some discrete groups. Therefore, we propose an Interval Length-Gini Discretization (LGD) method which considers the groups and misclassified cases to obtain the best hypothesis for a given set of data. After an appropriate discretization, we propose one-cut and memory-iteration-based approach to select a set of useful sensor-value pairs for reducing the model size by removing redundant features and guaranteeing an acceptable accuracy. In the experiments our framework has a good performance on real data set collected from 50 participants in eight months, and a smaller size than the existing classifications. |
URI: | http://dx.doi.org/10.1007/978-3-319-13186-3_29 http://hdl.handle.net/11536/125147 |
ISBN: | 978-3-319-13186-3; 978-3-319-13185-6 |
ISSN: | 0302-9743 |
DOI: | 10.1007/978-3-319-13186-3_29 |
期刊: | TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING |
Volume: | 8643 |
起始頁: | 305 |
結束頁: | 317 |
顯示於類別: | 會議論文 |