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.

The university philosopher in early modern Germany

In this groundbreaking collection of essays the history of philosophy appears in a new light, not as reason's progressive discovery of its universal conditions, but as a series of unreconciled disputes over the proper way to conduct oneself as a philosopher. By shifting focus from the philosopher as proxy for the universal subject of reason to the philosopher as a... More special persona arising from rival forms of self-cultivation, philosophy is approached in terms of the social office and intellectual deportment of the philosopher, as a personage with a definite moral physiognomy and institutional setting. In so doing, this collection of essays by leading figures in the fields of both philosophy and the history of ideas provides access to key early modern disputes over what it meant to be a philosopher, and to the institutional and larger political and religious contexts in which such disputes took place.