A Study of Cross-National Differences in Happiness Factors Using Machine Learning Approach

作者：Saputri Theresia Ratih Dewi; Lee Seok Won^*

来源：International Journal of Software Engineering and Knowledge Engineering, 2015, 25(9-10): 1699-1702.

DOI：10.1142/S0218194015710023

• Data mining
• classification
• feature selection
• principal component analysis
• support vector machine

National happiness has been actively studied throughout the past years. The happiness factor varies due to different human perspectives. The factors used in this work include both physical needs and the mental needs of humanity, for example, the educational factor. This work identified more than 90 features that can be used to predict the country happiness. Due to numerous features, it is unwise to rely on the prediction of national happiness by manual analysis. Therefore, this work used a machine learning technique called Support Vector Machine (SVM) to learn and predict the country happiness. In order to improve the prediction accuracy, dimensionality reduction technique which is the information gain was also used in this work. This technique was chosen due to its ability to explore the interrelationships among a set of variables. Using data of 187 countries from the UN Development Project, this work is able to identify which factor needed to be improved by a certain country to increase the happiness of their citizens.

• 出版日期2015-12