Questionnaires-based skin attribute prediction using Elman neural network
Introduction
Skin attribute testing is an important task in daily cosmetics development. The traditional clinical skin attribute test is carried out by clinical experiments, which not only require large amounts of information, complicated processes, but also need expensive specialized equipment. Therefore, a rapid and low cost test method is needed for skin attribute evaluation in cosmetics stores and other places. Fortunately, information technology has given hope to cosmetics company research and development (R&D) to predict skin attributes based on existing experimental data in a new approach.
Boelsma et al. [1] have used questionnaires to evaluate whether or not those features they are interested in are associated with the skin attribute of humans. Grove et al. [2] have also used questionnaires to evaluate the products' effects on the skin attributes in the R&D process. However, to the best of our knowledge, no one has realized questionnaire-based skin attribute prediction with a data mining method. Most of the current work achieves skin attribute prediction through the use of questionnaires without data mining methods. They usually select questions according to their own experiences. In the prediction process, simple logic is used to judge a portion of answers. Those prediction methods are linear predictors, while skin attribute is a nonlinear function of its input features, and the results in the existing work are usually inaccurate.
In this study, a novel prediction approach based on recurrent neural network models is proposed for participants’ skin attribute prediction. Three realization steps must be completed to achieve skin attribute prediction with data mining methods in our prediction system. The first is digitizing the existing experimental data and questionnaires, and transforming them into numeric or several nominal data that can be processed by the computer. Then the key features of skin attributes are selected with the digitalized data. With the key features, the prediction models for skin attributes are constructed. Women's skin attributes can be predicted by computer directly with these models. In response to these problems, we give a total solution by using the data preprocessing, feature selection and prediction methods in the area of data mining.
This new prediction approach has three main characteristics. The first one is that it saves time. The only thing participants must do is fill out the questionnaire instead of taking complex clinical tests. The second is that it saves cost. The results are given by computer immediately without complex manual analysis. The third is that this prediction approach is very simple. Participants can complete the questionnaire at their home via the internet. This new approach allows participants to understand their skin attributes much easier. Skin attribute research work will also become easier.
The remaining parts of this paper are organized as follows: Section 2 covers some related work including data preprocessing methods, feature selection methods and the neural network model. The design of the proposed prediction model is presented in Section 3. The system implementation is shown in Section 4. In Section 5, the experimental results are shown and evaluated. Finally, Section 6 presents our conclusions.
Section snippets
Related work
In this section, we survey some related research techniques in the area of data preprocessing, feature selection and prediction. Before skin attribute prediction is implemented with the existing questionnaires and clinical experimental data, data must be preprocessed. Information needs to be extracted from the original questionnaires so that it can be handled by computer. A brief analysis of distribution of the original data also needs to be made. After preprocessing, key features are selected
Data preprocessing
The raw questionnaire data must be transformed into the form that can be handled by computer, so data preprocessing is very important before key features selection. The work in this paper is based on two questionnaires. One is focused on the testers' basic information, such as age, educational background, life habits and occupations. The other investigates the testers' habits of using sunscreen and how much she knows about UV products. The testers' skin attributes (Tone, Spots and Hydration)
System implementation
Based on the preceding skin attribute prediction method, a skin attribute prediction system for Chinese women has been developed. In the preparation phase of the system, these questionnaires about the Chinese women's skin attributes are preprocessed first. Then key features are selected from the processed data. According to different skin attributes' key features, the corresponding data is normalized to obtain the training data set, with which different skin attributes’ prediction models are
Key features
In this paper, the questionnaires are filled out by Chinese women living in the urban area of Beijing and Guangzhou. The original questionnaires have a total of 49 questions and contain 905 instances. These data are used to select key features of three different targets—Tone, Spots, and Hydration. In the original questionnaires, one question asks whether the tester used facial moisturizer today or not. Because facial moisturizer may make obvious changes to the tester's hydration, we removed the
Conclusions
In this paper, we propose a novel prediction approach based on neural network model that helps predict the skin attributes for participants. This new prediction approach contributes a great deal to skin attribute testing, which reduces much of the time and costs needed to conduct experiments. If a participant wants to test her skin attributes, the only thing she needs to do is complete the questionnaire instead of undergoing traditional complex clinical tests. This prediction approach is so
Acknowledgments
This work is supported by P&G–Tsinghua Collaboration Research Funding, National Natural Science Foundation of China (Grant No. 60875073), and Important National Science & Technology Specific Projects (Grant No. 2009ZX02001). We would like to thank Amy Chastain for reviewing and polishing the language of this paper. Thanks also to all reviewers for their constructive comments and guidance in shaping this paper.
Wei Wan is currently pursuing his master degree in the State Key Laboratory on Intelligent Technology and Systems, Department of Computer Science and Technology at Tsinghua University in Beijing, China. He also graduated from the Department of Computer Science and Technology at Tsinghua University in 2009. His research interests span the areas of parallel processing, data mining, machine learning, and natural language processing.
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Wei Wan is currently pursuing his master degree in the State Key Laboratory on Intelligent Technology and Systems, Department of Computer Science and Technology at Tsinghua University in Beijing, China. He also graduated from the Department of Computer Science and Technology at Tsinghua University in 2009. His research interests span the areas of parallel processing, data mining, machine learning, and natural language processing.
Prof. & Dr. Hua Xu received his B.S. from Xi'an Jiaotong University in 1998. He received his M.S. and Ph.D. from Tsinghua University in 2000 and 2003, respectively. Now he is working in dept. of C.S., Tsinghua University. His research fields include the following aspects: data mining, intelligent information processing and advanced process controllers for IC manufacturing equipments. He has published over 30 academic papers, received 3 invention patents of advanced controller and is also the copyright owner of 5 software systems.
Wenhao Zhang is a Ph.D. student in Department of Computer Science and Technology in Tsinghua University in Beijing, China. Previously he received his B.E. in Software Engineering from Software School of Dalian University of Technology in 2010. His current research interests are on data mining, and semantic web, natural language processing, in particular in sentiment analysis.
Dr. Xincheng Hu received his Ph.D. in Environmental Chemistry, from Osaka Prefecture University, Japan. Now he is working in Skin Care in P&G Technology (Beijing) Co., Ltd. engaged in the research and development for skin care product formulation, clinical study for 16 years.
Dr. Deng Gang received his B.S., Master and Ph.D from Nankai University. Joined Procter & Gamble company in 2006, senior scientist in clinical function. His research fields include the following aspects: Asian female and male skin understanding, whitening active screening, skin modeling, and skin measuring technology development. He has published over 15 academic papers in biology area.