Student characteristics and academic success: An econometric study of the performance of "equity groups"

Like previous volumes in the Educational Innovation in Economics and Business Series, this book is genuinely international in terms of its coverage. With contributions from nine different countries and three continents, it reflects a global interest in, and commitment to, innovation in business education, with a view to enhancing the learning experience of both undergraduates and postgraduates. It should prove of value to anyone engaged directly in business education, defined broadly to embrace management, finance, marketing, economics, informational studies, and ethics, or who has responsibility for fostering the professional development of business educators. The contributions have been selected with the objective of encouraging and inspiring others as well as illustrating developments in the sphere of business education. This volume brings together a collection of articles describing different aspects of the developments taking place in today’s workplace and how they affect business education. It describes strategies for breaking boundaries for global learning. These target specific techniques regarding teams and collaborative learning, transitions from academic settings to the workplace, the role of IT in the learning process, and program-level innovation strategies. This volume addresses issues faced by professionals in higher and further education and also those involved in corporate training centers and industry.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.