User Classification in Electronic Devices Using Machine Learning Methods User classification is a major concern for electronic device providers, because accurate and efficient classification can cut operating cost of company significantly. To deal with this problem, manufacturers try to classify customer into several categories and recognize the characteristic of users, then adopt different promotion strategies to improve sales revenue. This study aims to build models to divide users into several categories, and identify critical and controllable features which dramatically affects classification results. We test proposed models with real data coming from an electronic device producer. Results shows that random forest model performs best. Our main contributions are: 1) we focus on user classification of electronic devices; although many existing studies have discussed similar problem, few of them focus on applications in electronic devices; 2) we consider imbalance sample, and datasets are from real company. This work will be helpful for electronic device producers to improve operation and enhance marketing competitiveness.

Microblog User Profiling: A Supervised Clustering based Approach for Age and Gender Classification User profiling is the process of tagging user attributes, such as gender, age, location and so on. Currently the popular way is to do classification by treating attributes as labels. In addition, ensemble classifications are used to future improve classification quality. However, the base classifier of traditional ensemble classification is trained by using random sampling, which cannot guarantee a constantly good classification performance. This paper proposes a supervised clustering of ensemble classification approach to tag microblog users with age and gender attributes. The proposed approach is to divide the training data with a same label into multiple clusters and then combine different clusters from training data with different labels into training data subsets. Each subset is used to train a base classifier. Experimental results show that the proposed approach can improve the gender and age classification accuracy classification by 1.79 % and 0.67 % respectively, compared with the traditional ensemble classification approach.