A Classifier Capable of Handling Incomplete Data Set

Mining Incomplete Data - A Rough Set Approach

Handling Incomplete Categorical Data for Supervised Learning

Classification is an important research topic in knowledge discovery. Most of the researches on classification concern that a complete dataset is given as a training dataset and the test data contain all values of attributes without missing. Unfortunately, incomplete data usually exist in real-world applications. In this paper, we propose new handling schemes of learning classification models f...

A Novel Integrated Classifier for Handling Data Warehouse Anomalies

Within databases employed in various commercial sectors, anomalies continue to persist and hinder the overall integrity of data. Typically, Duplicate, Wrong andMissed observations of spatial-temporal data causes the user to be not able to accurately utilise recorded information. In literature, different methods have been mentioned to clean data which fall into the category of either determinist...

An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels

Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To ...

Image Reconstruction from Incomplete Data