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Paper details
Number 4 - December 2016
Volume 26 - 2016
Lookahead selective sampling for incomplete data
Loai Abdallah, Ilan Shimshoni
Abstract
Missing values in data are common in real world applications. There are several methods that deal with this problem.
In this paper we present lookahead selective sampling (LSS) algorithms for datasets with missing values. We developed
two versions of selective sampling. The first one integrates a distance function that can measure the similarity between
pairs of incomplete points within the framework of the LSS algorithm. The second algorithm uses ensemble clustering
in order to represent the data in a cluster matrix without missing values and then run the LSS algorithm based on the
ensemble clustering instance space (LSS-EC). To construct the cluster matrix, we use the k-means and mean shift clustering algorithms especially modified to deal with incomplete datasets. We tested our algorithms on six standard numerical datasets from different fields. On these datasets we simulated missing values and compared the performance of the LSS and LSS-EC algorithms for incomplete data to two other basic methods. Our experiments show that the suggested selective sampling algorithms outperform the other methods.
Keywords
selective sampling, missing values, ensemble clustering