A comparison of two types of rough sets induced by coverings

Rough set theory is an important technique in knowledge discovery in databases. In covering-based rough sets, many types of rough set models were established in recent years. In this paper, we compare the covering-based rough sets defined by Zhu with ones defined by Xu and Zhang. We further explore the properties and structures of these types of rough set models. We also consider the reduction of coverings. Finally, the axiomatic systems for the lower and upper approximations defined by Xu and Zhang are constructed

Blueprint Buffalo Action Plan: Regional Strategies for Reclaiming Vacant Properties in the City and Suburbs of Buffalo

Over a period of about nine months, the NVPC team conducted interviews and gathered insights that have resulted in this report. During the study period, Buffalo–Niagara emerged as a region broadly challenged by decades of disinvestment and population loss, but also as a close network of communities singularly blessed with a wealth of historic, transit-friendly, and affordable neighborhoods and commercial areas. Building on the City of Buffalo’s “asset management” strategy first proposed in 2004 by the Cornell Cooperative Extension Association—and now formally adopted by the Buffalo Common Council as part of its comprehensive 20-year plan for the city—the NVPC team sought to reexamine how the revitalization of Buffalo’s vacant properties could actually serve as a catalyst to address the region’s other most pressing problems: population loss, a weak real estate market in the inner city, signs of incipient economic instability in older suburbs, quality-of-life issues, school quality, and suburban sprawl

Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce

IEEE Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility