45,041 research outputs found
Research on Rough Set Model Based on Golden Ratio
AbstractHow to make decision with pre-defined preference-ordered criteria also depends on the environment of the problem. Dominance rough set model is suitable for preference analysis and probabilistic rough set introduces probabilistic approaches to rough sets. In this paper, new dominance rough set rough set models are given by taking golden ratio into account. Also, we present steps to make decision using new dominance rough set models
Analysing imperfect temporal information in GIS using the Triangular Model
Rough set and fuzzy set are two frequently used approaches for modelling and reasoning about imperfect time intervals. In this paper, we focus on imperfect time intervals that can be modelled by rough sets and use an innovative graphic model [i.e. the triangular model (TM)] to represent this kind of imperfect time intervals. This work shows that TM is potentially advantageous in visualizing and querying imperfect time intervals, and its analytical power can be better exploited when it is implemented in a computer application with graphical user interfaces and interactive functions. Moreover, a probabilistic framework is proposed to handle the uncertainty issues in temporal queries. We use a case study to illustrate how the unique insights gained by TM can assist a geographical information system for exploratory spatio-temporal analysis
A Novel Approach of Rough Conditional Entropy-Based Attribute Selection for Incomplete Decision System
Pawlak's classical rough set theory has been applied in analyzing ordinary information systems and decision systems. However, few studies have been carried out on the attribute selection problem in incomplete decision systems because of its complexity. It is therefore necessary to investigate effective algorithms to deal with this issue. In this paper, a new rough conditional entropy-based uncertainty measure is introduced to evaluate the significance of subsets of attributes in incomplete decision systems. Furthermore, some important properties of rough conditional entropy are derived and three attribute selection approaches are constructed, including an exhaustive search strategy approach, a heuristic search strategy approach, and a probabilistic search strategy approach for incomplete decision systems. Moreover, several experiments on real-life incomplete data sets are conducted to assess the efficiency of the proposed approaches. The final experimental results indicate that two of these approaches can give satisfying performances in the process of attribute selection in incomplete decision systems
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets
Recent advances in computer vision have led to significant progress in the
generation of realistic image data, with denoising diffusion probabilistic
models proving to be a particularly effective method. In this study, we
demonstrate that diffusion models can effectively generate fully-annotated
microscopy image data sets through an unsupervised and intuitive approach,
using rough sketches of desired structures as the starting point. The proposed
pipeline helps to reduce the reliance on manual annotations when training deep
learning-based segmentation approaches and enables the segmentation of diverse
datasets without the need for human annotations. This approach holds great
promise in streamlining the data generation process and enabling a more
efficient and scalable training of segmentation models, as we show in the
example of different practical experiments involving various organisms and cell
types.Comment: 9 pages, 2 figure
Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts
In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing ‘hidden’ or not easily discernible data concepts. PLSA provides a ‘bottom up’ approach to interoperability problems for users in the face of ‘top down’ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches
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