Vaguely Quantified Rough Sets

The hybridization of rough sets and fuzzy sets has focused on creating an end product that extends both contributing computing paradigms in a conservative way. As a result, the hybrid theory inherits their respective strengths, but also exhibits some weaknesses. In particular, although they allow for gradual membership, fuzzy rough sets are still abrupt in a sense that adding or omitting a single element may drastically alter the outcome of the approximations. In this paper, we revisit the hybridization process by introducing vague quantifiers like "some" or "most" into the definition of upper and lower approximation. The resulting vaguely quantified rough set (VQRS) model is closely related to Ziarko's variable precision rough set (VPRS) model

Representative Set of Objects in Rough Sets Based on Galois Connections

This paper introduces a novel definition, called representative set of objects of a decision class, in the framework of decision systems based on rough sets. The idea behind such a notion is to consider subsets of objects that characterize the different classes given by a decision system. Besides the formal definition of representative set of objects of a decision class, we present different mathematical properties of such sets and a relationship with classification tasks based on rough sets. © 2020, Springer Nature Switzerland AG

A Three-Way Decision Approach to Email Spam Filtering

Abstract. Many classification techniques used for identifying spam emails, treat spam filtering as a binary classification problem. That is, the in-coming email is either spam or non-spam. This treatment is more for mathematical simplicity other than reflecting the true state of nature. In this paper, we introduce a three-way decision approach to spam filtering based on Bayesian decision theory, which provides a more sensible feed-back to users for precautionary handling their incoming emails, thereby reduces the chances of misclassification. The main advantage of our ap-proach is that it allows the possibility of rejection, i.e., of refusing to make a decision. The undecided cases must be re-examined by collect-ing additional information. A loss function is defined to state how costly each action is, a pair of threshold values on the posterior odds ratio is systematically calculated based on the loss function, and the final deci-sion is to select the action for which the overall cost is minimum. Our experimental results show that the new approach reduces the error rate of classifying a legitimate email to spam, and provides better spam pre-cision and weighted accuracy. Key words: spam filter, three-way decision, naive Bayesian classifica-tion, Bayesian decision theory, cost

A rough set-based association rule approach implemented on exploring beverages product spectrum

[[abstract]]When items are classified according to whether they have more or less of a characteristic, the scale used is referred to as an ordinal scale. The main characteristic of the ordinal scale is that the categories have a logical or ordered relationship to each other. Thus, the ordinal scale data processing is very common in marketing, satisfaction and attitudinal research. This study proposes a new data mining method, using a rough set-based association rule, to analyze ordinal scale data, which has the ability to handle uncertainty in the data classification/sorting process. The induction of rough-set rules is presented as method of dealing with data uncertainty, while creating predictive if—then rules that generalize data values, for the beverage market in Taiwan. Empirical evaluation reveals that the proposed Rough Set Associational Rule (RSAR), combined with rough set theory, is superior to existing methods of data classification and can more effectively address the problems associated with ordinal scale data, for exploration of a beverage product spectrum.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子