A Distance-Based Method for Attribute Reduction in Incomplete Decision Systems

There are limitations in recent research undertaken on attribute reduction in incomplete decision systems. In this paper, we propose a distance-based method for attribute reduction in an incomplete decision system. In addition, we prove theoretically that our method is more effective than some other methods.

Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems

A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets.

On the Time Complexity of the Problem Related to Reducts of Consistent Decision Tables

In recent years, rough set approach computing issues concerning reducts of decision tables have attracted the attention of many researchers. In this paper, we present the time complexity of an algorithm computing reducts of decision tables by relational database approach. Let DS = (U, C ∪ {d}) be a consistent decision table, we say that A ⊆ C is a relative reduct of DS if A contains a reduct of DS. Let s = be a relation schema on the attribute set C ∪ {d}, we say that A ⊆ C is a relative minimal set of the attribute d if A contains a minimal set of d. Let Qd be the family of all relative reducts of DS, and Pd be the family of all relative minimal sets of the attribute d on s. We prove that the problem whether Qd ⊆ Pd is co-NP-complete. However, the problem whether Pd ⊆ Qd is in P .

Paleotemperatures, stable isotopes and alkenones of samples from the South Atlantic

Carbon dioxide is one of the most important greenhouse gases which are increasing in atmospheric concentration due to human activities. For using natural CO2 dynamics as a key to understanding the climatic consequences of anthropogenic pCO2 rise, the ocean plays an important role due to its much larger carbon pool compared to the atmosphere. By studying the ratio of stable carbon isotopes in organic matter from marine sediments, it is possible to estimate the partial pressure of CO2 in surface waters during ancient times. The organic compound C37:2 alkenone, whose sole origin is from autotrophic marine algae, was chosen for d13C analysis and its isotopic composition used to reconstruct past PCO2 levels in the surface layer of the eastern Angola Basin for the last 200,000 years. In addition to the variation of ancient concentrations of dissolved CO2 ([CO2(aq)] = ce), the effect of carbon demand which depends on algal growth rate was considered. Here to, carbon isotopic fractionation of C37:2 alkenones (ep) in core-top sediments from the equatorial and the South Atlantic was calibrated against pre-industrial [CO2(aq)] and phosphate concentrations in surface waters. From these data, a variable b = (25 per mil - ep) * ce which reflects intracellular carbon demand was calculated. This variable b correlates with the ambient concentration of seawater phosphate and depends on growth rates. The bulk sediment d15N was used as a proxy parameter for calculating ancient b-values, taking into account that d15N in core-top sediments is correlated to phosphate concentration in modern surface waters. On this basis, the alkenone d13C record of GeoB1016-3 documents a permanent oceanic source for atmospheric carbon dioxide during the last 200,000 years. As a consequence of using d15N derived b-values instead of b = constant, the Angola Basin appears to have been an even stronger CO2 source during glacial periods than at present. Qualitatively similar results were reported by Jasper et al. (1994) for the central Equatorial Pacific. These observations suggest that enhanced productivity of low-latitude upwelling areas during glacial periods is not responsible for the lower CO2 content of the glacial atmosphere.