in this article, lindley's measure of average information is used to measure the information contained in incomplete observations on the vector of unknown regression coefficients [9]. this measure of information may be used to compute the missing regressor values.
In this article, Lindley's measure of average information is used to measure the information contained in incomplete observations on the vector of unknown regression coefficients [9]. This measure of information may be used to compute the missing regressor values.
In the paper nine different approaches to missing attribute values are presented and compared. Ten input data files were used to investigate the performance of the nine methods to deal with missing attribute values. For testing both naive classification and new classification tec
In a previous paper three types of missing attribute values: lost values, attributeconcept values and “do not care” conditions were compared using six data sets. Since previous experimental results were affected by large variances due to conducting experiments on different versio
Real-life data sets are often incomplete, i.e., some attribute values are missing. In this paper we compare traditional, frequently used methods of handling missing attribute values, which are based on preprocessing, with another class of methods dealing with missing attribute va
Recently, results on a comparison of seven successful methods of handling missing attribute values were reported. This paper describes experimental results on the three most successful methods out of these seven. Two of these methods, based on a Closet Fit idea (searching in a re
A new approach to missing attribute values, based on the idea of an attribute-concept value, is studied in the paper. This approach, together with two other approaches to missing attribute values, based on "do not care" conditions and lost values are discussed using rough set met