The skyline query has proven to be an important tool in multi-criteria decision making and search space pruning. A skyline query returns the subset of points from a multidimensional dataset that are not dominated by any other point. Due to its wide applications, skyline query and its variants have been extensively studied in the past. However, skyline computation for incomplete domain, where po...

One of the most difficult problems confronting investigators who analyze data from surveys is how to treat missing data. Many statistical procedures cannot be used immediately if any values are missing. Imputation of missing data before starting statistical analysis is then necessary. This paper proposes imputation methods of the mean based on indirect estimators of available cases. A complete ...

This study investigates the impact of available training alternatives (TAs) on employee retention in small and medium enterprises (SMEs). A noticeable problem with this research issue is that individual SMEs may utilize different combination of TAs. The considered survey questionnaire allowed respondent SME owners/managers the option to gauge the level of satisfaction of a TA or to indicate tha...

An essential component in Machine Learning processes is to estimate any uncertainty measure re¯ecting the strength of the relationships between variables in a dataset. In this paper we focus on those particular situations where the dataset has incomplete entries, as most real-life datasets have. We present a new approach to tackle this problem. The basic idea is to initially estimate a set of p...

Linked (Open) Data (LD) offer the great opportunity to interconnect and share large amounts of data on a global scale, creating added value compared to data published via pure HTML. However, this enormous potential is not completely accessible. In fact, LD datasets are often affected by errors, inconsistencies, missing values and other quality issues that may lower their usage. Users are often ...

We show how it is possible to use the Kohonen self-organizing algorithm to deal with data with missing values and estimate them. After a methodological reminder, we illustrate our purpose with three applications to real-world data. Nous montrons comment il est possible d’utiliser l’algorithme d’autoorganisation de Kohonen pour traiter des données avec valeurs manquantes et estimer ces dernières...

Streams of data often originate from many distributed sources. A distributed stream processing system publishes such streams of data and enables queries over the streams. This allows users to retrieve and relate data from the distributed streams without needing to know where they are located. Stream data is important not only for its current values but also for past values produced. In order to...

Data pre-processing is a critical task in the knowledge discovery process in order to ensure the quality of the data to be analyzed. One widely studied problem in data pre-processing is the handling of missing values with the aim to recover its original value. Based on numerous studies on missing values, it is shown that different methods are needed for different types of missing data. In this ...

In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations show that, ...