THE INCIDENCE AND WAGE EFFECTS OF OVEREDUCATION: THE CASE OF TAIWAN

This paper, based on data from Survey of Family Income and Expenditure of Taiwan, shows that the recent trends of job match in Taiwan labor market have been marked by increasing proportion of overeducated workers due to the higher education expansion policy, while the incidence of undereducation continues to decline. Furthermore, workers¡¯ economic position is not completely determined by their educational levels. Working experience also plays an important role in workers¡¯ job placement and their wages. Workers with relatively less working experience are more likely to be overeducated, while workers with relatively more working experience are more likely to be undereducated. Overeducated (Undereducated) workers would earn more (less) than their co-workers with adequate education but less (more) than the workers having the same educational level with adequate education for jobs. However, the rewards (penalties) to adequate education and overeducation (undereducation) decline as more experience accumulated. Evidence also shows effect of bumping down from overeducation on the wages and employment of lower educated workers.Overeducation, Wage, Bumping Down, Labor Market, Taiwan

Soft Methodology for Cost-and-error Sensitive Classification

Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms. We also demonstrate that the methodology can be extended for considering the weighted error rate instead of the original error rate. This extension is useful for tackling unbalanced classification problems.Comment: A shorter version appeared in KDD '1

PREFERENCE TEST OF THE WEIGHTED SHOES

The purpose of this study was to use paired comparison approach to test the weighted shoe preference of the subjects. Forty subjects were recruited to put on five different weighted shoes and choose the preferred one after completing four paired comparisons. During the test, subjects were blind of any information from the shoes. The results showed that thirty-two (80%) out of the forty subjects preferred Shoe D or E, which centre of mass was close to the rear end of the shoe. Significant difference was found in shoe preference between the males and females (?24=10.500, p=.033), while was not found between the lighters and heaviers (?24=5.583, p=.233). The mechanism of the gender effect on the preference decision are unclear. The results of the weighted shoe preference test could be applied to athlete training or rehabilitation shoe design to be comfortable for the users

Interpretations of Domain Adaptations via Layer Variational Analysis

Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method.Comment: Published at ICLR 202