Rough sets theory and uncertainty into information system

This article is focused on rough sets approach to expression of uncertainty into information system. We assume that the data are presented in the decision table and that some attribute values are lost. At first the theoretical background is described and after that, computations on real-life data are presented. In computation we wok with uncertainty coming from missing attribute values

The effect of missing values using genetic programming on evolvable diagnosis

Medical databases usually contain missing values due the policy of reducing stress and harm to the patient. In practice missing values has been a problem mainly due to the necessity to evaluate mathematical equations obtained by genetic programming. The solution to this problem is to use fill in methods to estimate the missing values. This paper analyses three fill in methods: (1) attribute means, (2) conditional means, and (3) random number generation. The methods are evaluated using sensitivity, specificity, and entropy to explain the exchange in knowledge of the results. The results are illustrated based on the breast cancer database. Conditional means produced the best fill in experimental results

Solving Incomplete Datasets in Soft Set Using Supported Sets and Aggregate Values

AbstractThe theory of soft set proposed by Molodtsovin 1999[1]is a new method for handling uncertain data and can be defined as a Boolean-valued information system. Ithas been applied to data analysis and decision support systems based on large datasets. In this paper, it is shown that calculated support value can be used to determine missing attribute value of an object. However, in cases when more than one value is missing, the aggregate values and calculated support values will be used in determining the missing values. By successfully recovering missing attribute values, the integrity of a dataset can still been maintained

Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network

With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer from being absent due to privacy-protecting policies or copyright restrictions during data collection. The absence of graph data can be roughly categorized into attribute-incomplete and attribute-missing circumstances. Specifically, attribute-incomplete indicates that a part of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that the whole attribute vectors of partial nodes are missing. Although many efforts have been devoted, none of them is custom-designed for a common situation where both types of graph data absence exist simultaneously. To fill this gap, we develop a novel network termed Revisiting Initializing Then Refining (RITR), where we complete both attribute-incomplete and attribute-missing samples under the guidance of a novel initializing-then-refining imputation criterion. Specifically, to complete attribute-incomplete samples, we first initialize the incomplete attributes using Gaussian noise before network learning, and then introduce a structure-attribute consistency constraint to refine incomplete values by approximating a structure-attribute correlation matrix to a high-order structural matrix. To complete attribute-missing samples, we first adopt structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by adaptively aggregating the reliable information of attribute-incomplete samples according to a dynamic affinity structure. To the best of our knowledge, this newly designed method is the first unsupervised framework dedicated to handling hybrid-absent graphs. Extensive experiments on four datasets have verified that our methods consistently outperform existing state-of-the-art competitors