The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We focus on a questionnaire consisting of three-choice question or multiple-choice question, and propose a privacy-preserving questionnaire by non-deterministic information. Each respondent usually answers one choice from the multiple choices, and each choice is stored as a tuple in a table data. The organizer of this questionnaire analyzes the table data set, and obtains rules and the tendency. If...
We have proposed a framework named Rough Non-deterministic Information Analysis (RNIA), and developed two software tools, RNIA in Prolog and getRNIA in Python. In order to handle big data sets, we newly employ SQL and PHP. This paper reports the current state of the software tool in SQL, which is the preliminary version for NIS-Apriori in SQL.
Rough sets and rule induction are formulated by directly using indiscernibility relations in information tables. First, we describe them in information tables with complete information. Second, they are shown on the basis of possible world semantics from the viewpoint of certainty and possibility, as was done by Lipski in the field of databases, in order to examine the fundamentals of rough sets and...
We reconsider information dilution, which was originally proposed in [4]. This adds the noises to the value in table data in order to hide the actual value, namely information dilution is a kind of information hiding in table data. In this paper, we pick up lenses data set ϕ in UCI machine learning repository, and we dilute this data set ϕ with preserving obtainable rules.
How rules are induced on the basis of rough sets under Lipski's approach has been examined in a possibilistic information system where attribute values in information tables are expressed by normal possibility distributions. In Lipski's approach possible tables are created from the original information table and each possible table has a possibilistic degree with which it is the actual information...
This paper briefly surveys rough set-based framework RNIA (Rough Non-deterministic Information Analysis), which can handle tables with non-deterministic data. Each rough set-based concept in the standard tables is redefined based on the possible world semantics. Especially, in rule generation, we reconsider the definition of rules, because the standard definition of rule generation cannot be applicable...
How rules are induced on the basis of rough sets has been examined in possibilistic information systems where attribute values are expressed by normal possibility distributions. We cannot obtain the unique membership degree of an object for rough approximations in the possibilistic information systems. Instead, we can derive certain and possible membership degrees to which an object certainly and...
We have been proposing a framework Rough Non-deterministic Information Analysis (RNIA), which applies granular computing concepts to tables with incomplete information. We have recently defined an expression named division chart over an equivalence class with respect to descriptors. A division chart takes the similar role of the contingency table. In this paper, we clarify the relation between a division...
Rough sets have been examined in possibilistic information systems where attribute values are expressed by normal possibility distributions. Like incomplete information systems, rough approximations, which consist of lower and upper approximations, are essentially dual. Rough approximations are derived from dealing with certain rough approximations as well as possible ones that are lower and upper...
We have been coping with several aspects of rough sets in Non-deterministic Information Systems (NISs). We are simply calling this work Rough Non-Deterministic Information Analysis (RNIA). This paper newly considers Lipski's Incomplete Information Databases (LIIDs) which can be seen as NISs with intervals, and proposes new decision making in LIIDs. A granular computing concept is applied to intervals,...
In rough set theory, the concept of the consistency is characterized by the inclusion relation of equivalence classes. This property connects rough sets with granular computing. In this paper, we consider properties on inclusion relations, and propose a diagram named a division chart. We examine several properties in five cases. This division chart shows us visual information about inclusion relations...
A method of possible equivalence classes was developed to deal with missing values. To deal with imprecision of rough approximations that comes from missing values, the concepts of certainty and possibility were used. When an information table contains the missing values, two rough approximations, certain and possible ones, are obtained. The actual rough approximation is located between the certain...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.