On Selecting Feature-Value Pairs on Smart Phones for Activity Inferences

Abstract

Prior works have been elaborated on activity inferences from context information sensed on smart phones. Most of sensing computations are performed on CPU of smart phones. Thus, Sensor Hub is designed to avoid CPU involvement. However, Sensor Hub has several limitations, such as limited memory space and computation power. Since activity inference is a classification problem, prior works have already proposed some classifiers on smart phones. However, if one would like to build a classifier model in Sensor Hub, one challenging issue is to reduce the model size. One approach to reduce the model size of classifiers is feature selection. Feature selection reduces the model size by removing features in the feature set. Nonetheless, because sensor space is limited, removing features could reduce accuracy of classifier algorithm significantly. Therefore we explore feature-value selection concept, which considers the value rather than the feature to reduce the model size while preserving accuracy of classifiers. In this paper, we propose three feature-value selection methods, which consider confusion and redundancy among the feature-value. Due to the nature of feature-values, discretization of sensor data is important. We design a discretization method, LGD (Length Gini Discretization) and compare it with another method, MDLP (Minimum Description Length Discretization), to discretize sensor data using confusion metric to choose the cut point. Extensive experiments are conducted to evaluate our proposed feature-value selection methods. Feature-value removal allows us to reduce up to 80% of the model size and maintain average accuracy performance to 86%.