Motivation. Building reliable systems over partially complete data poses significant challenges because queries they send to the available data retrieve answers that may significantly differ from the real answers. This may lead to a wrong understanding of the data and the events and processes it describes. This problem is especially critical for analytical systems that aggregate retrieved data ...

In a sun-exposure study, questionnaires concerning sunhabits were collected from 195 subjects. This paper focuses on the general problem of missing data values, which occurs when some, or even all of the questions have not been answered in a questionnaire. Here, only missing values of low concentration are investigated. We consider and compare two di erent models for imputating missing values: ...

In this paper, the author at first briefly outlines the value of data in organizations and the opportunities and challenges in Afghanistan. Then the author takes the Kankor (National University Entrance Exam) data, particularly names of participants, locations, high schools and higher education institutions into account and explains how these data, that organizations in Afghanistan do not use f...

Visualization of incomplete data allows to simultaneously explore the data and the structure of missing values. This is helpful for learning about the distribution of the incomplete information in the data, and to identify possible structures of the missing values and their relation to the available information. The main goal of this contribution is to stress the importance of exploring missing...

This study demonstrates a novel form of business analytics, respecting the quality of the data available (allowing incompleteness in the data set), as well as engaging with the uncertainty in the considered outcome variable (inclusive of Don’t Know (DK) responses). The analysis employs the NCaRBS technique, based on the Dempster–Shafer theory of evidence, to investigate the relationship between...

Traditionally, the information system is assumed to be perfect, i.e. attribute values are not missing and supposed to be precise. In fact, imperfect information system is always existent. In this paper, based on imperfect information system (include missing data and imprecise data), the concepts of indiscernibility and discernibility are defined, their important properties are given, and the re...

In this paper, a CBR approach that deals with missing data is presented. In the conversational ISOR system different knowledge sources are involved, including medical experts. In the case base rules and formulae are stored that support the restoration of numerous missing values. The task is to restore missing values in an observed medical data set. The presented method is used for a set of phys...

A Latent-Class Selection Model for Nonignorably Missing Data Most missing-data procedures assume that the missing values are ignorably missing or missing at random (MAR), which means that the probabilities of response do not depend on unseen quantities. Although this assumption is convenient, it is sometimes questionable. For example, questionnaire items pertaining to sensitive information (e.g...

Background. In this paper, we address the challenge of recovering a time sequence of counts from aggregated historical data. For example, given a mixture of the monthly and weekly sums, how can we find the daily counts of people infected with flu? In general, what is the best way to recover historical counts from aggregated, possibly overlapping historical reports, in the presence of missing va...

Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Nonnegative matrix factorization (NMF) is a low ra...