Attribute reduction based on maximal rules in decision formal context
نویسندگان
چکیده
منابع مشابه
Compacted decision tables based attribute reduction
This paper first points out that the reducts obtained from a simplified decision table are different from those obtained from its original version, and from a simplified decision table, we cannot obtain the reducts in the sense of entropies. To solve these problems, we propose the compacted decision table that can preserve all the information coming from its original version. We theoretically d...
متن کاملGeneralized 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 conditiona...
متن کاملMetric Based Attribute Reduction in Dynamic Decision Tables
In the past two decades, several results appeared on feature reduction applying rough set theory. However, most of these methods are implemented on static decision tables. Using a distance measure, in this paper we propose algorithms to find the reducts of decision tables when adding or deleting objects. Since we can avoid re-running the original algorithms over the entire set of objects, our m...
متن کاملAttribute Reduction in Utility-Based Decision-Theoretic Rough Set Models
Decision-theoretic rough set (DTRS) model, proposed by Yao in the early 1990’s, introduces Bayesian decision procedure and loss function in rough set theory. Considering utility function in decision processing, utility-based decision-theoretic rough set model (UDTRS) is given in this paper. The utility of the positive region, the boundary region and the negative region are obtained respectively...
متن کاملIRDDS: Instance reduction based on Distance-based decision surface
In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2014
ISSN: 1875-6891,1875-6883
DOI: 10.1080/18756891.2014.963972