An Active Feature Selection Strategy for DWT in Artificial Taste

Tao Liu,Mengya Wu,Dongqi Li,Yanbing Chen

doi:10.1155/2018/9709505

Abstract

A discrete wavelet transform (DWT) extracts meaningful information in a time-frequency domain and is a favorable feature extraction approach from pulse-like responses in large pulse voltammetry (LAPV) electronic tongues (e-tongue). A regular DWT generates lots of coefficients to describe signal details and approximations at different scales. Thus, coefficient selection is necessary to reduce the feature size. However, the common DWT-based feature selection follows a passive mode: manipulation through human experience or exhaustive trials. It is subjective, time consuming, and barely works in nonlaboratory conditions. In this paper, we present an active feature selection strategy consisting of a dispersion ratio computation and optimal searching search. To evaluate the performance of the proposed method, we prepared several beverage samples and performed experiments with a LAPV e-tongue. Meanwhile, the features of raw response, peak-inflection point, referenced DWT method, and our proposed method were presented to indicate the effects of the refined features of the proposed method. Furthermore, we utilized several classifiers such as the k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) to evaluate the improvement of recognition by the refined features. Compared with other regular feature extraction methods, the proposed method can automatically explore high-quality features with an acceptable feature size. Moreover, the highest average accuracy was achieved by the proposed method for each classifier. It is an alternative feature extraction approach for a LAPV e-tongue without any manipulation in real applications.

Highlights

An artificial taste system named electronic tongue (e-tongue) has become a potential approach for liquid-phase evaluations [1, 2]
Regarding SVM1, two discrete wavelet transform (DWT)-based methods achieved the same average recognition rates and clearly exceeded no feature extraction (NFE) and peak-inflection point method (PIPM) while relative power ratio (RPR)-DWT obtained the best rate with SVM2
As to the rates with random forest (RF) and k-nearest neighbor (k-NN), the DWT-based methods have performed much better than others

Summary

Introduction

An artificial taste system named electronic tongue (e-tongue) has become a potential approach for liquid-phase evaluations [1, 2]. A sensor array and the proper pattern recognition algorithm are the two main parts of an e-tongue. The sensor array imitates a human’s taste cells to sense substances, while the pattern recognition algorithm functions as the human brain to handle judgments. Compared with traditional chemical devices, e-tongues have evident advantages including lower cost, lower latency, and simpler operations. Wide applications, such as honey identification [3], rice discrimination [4], and beverage classification [5, 6], have been a concern in recent years. Scholars mainly focus their attention on the substances with specific aromatic flavors such as tea and liquor [7, 8] since the e-tongue identifications are more objective and reproducible than human judgments [9]

Objectives

Methods

Results

Conclusion