<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

Lookahead selective sampling for incomplete data

Management Information Systems The Max Stern Yezreel Valley College, Emek Yezraeel, 1930600, Israel

Department of Mathematics and Computer Science College of Sakhnin for Teacher Education, Sakhnin, 20173,

Department of Information Systems University of Haifa, Haifa, 199,

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Lookahead selective sampling for incomplete data"}

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...

Lookahead selective sampling for incomplete data

Article Category: Individual Article Information

Online veröffentlicht: 30. Dez. 2016

Seitenbereich: 871 - 884

Eingereicht: 15. Okt. 2015

Akzeptiert: 19. Juli 2016

DOI: https://doi.org/10.1515/amcs-2016-0062

© by Loai Abdallah

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.