A novel index measure imputation algorithm for missing data values: A machine learning approach

• Published in: 2012 IEEE International Conference on Computational Intelligence and Computing Research pp. 1 - 7
• Main Authors: Madhu, G., Rajinikanth, T. V.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2012
• Subjects: Accuracy; classification; Classification algorithms; Data mining; decision tree; Decision trees; Finite element analysis; Imputation; index measure; Indexes; Indexing; Machine learning algorithms; missing values; Nearest neighbor methods
• ISBN: 1467313424; 9781467313421
• DOI: 10.1109/ICCIC.2012.6510198

The problem of missing data in the real world datasets has very significant role in the real time data mining process and becomes more complex in large databases. The presence of missing values influences data set features and the class attributes, thus affecting the predictive accuracies of the classifiers. For the last one decade, many researchers have come out with different techniques for dealing with missing attribute values in databases with homogeneous and/or numeric attributes. In this research work, we proposed a new indexing measure to the imputation algorithm for missing data values of the attributes to compute the similarity measure between any two typical elements in the dataset. It can also be applied on any dataset be it a nominal and/or real. The proposed algorithm is evaluated by extensive experiments and comparison with KNNI, SVMI, WKNNI, KMI and FKMI algorithms. The results showed that the proposed algorithm has better performance than the existing imputation algorithms in terms of classification accuracy and also our decision tree algorithm employs highly accurate decision rules.