Prediction of Missing Values for Decision Attribute

Handling Missing Attribute Values

In this chapter methods of handling missing attribute values in data mining are described. These methods are categorized into sequential and parallel. In sequential methods, missing attribute values are replaced by known values first, as a preprocessing, then the knowledge is acquired for a data set with all known attribute values. In parallel methods, there is no preprocessing, i.e., knowledge...

Estimating Missing Attribute Values Using Dynamically-Ordered Attribute Trees

Classification performance can degrade if data contain missing attribute values. Many methods deal with missing information in a simple way, such as replacing missing values with the global or class-conditional mean/mode. We propose a new iterative algorithm to effectively estimate missing attribute values in both training data and test data. The attributes are selected one by one to be complet...

Handling Missing Attribute Values in Decision Tables Using Valued Tolerance Approach

Comparisons on Different Approaches to Assign Missing Attribute Values

A commonly-used and naive solution to process data with missing attribute values is to ignore the instances which contain missing attribute values. This method may neglect important information within the data, significant amount of data could be easily discarded, and the discovered knowledge may not contain significant rules. Some methods, such as assigning the most common values or assigning ...

Handling Missing Attribute Values in Preterm Birth Data Sets

The objective of our research was to find the best approach to handle missing attribute values in data sets describing preterm birth provided by the Duke University. Five strategies were used for filling in missing attribute values, based on most common values and closest fit for symbolic attributes, averages for numerical attributes, and a special approach to induce only certain rules from spe...