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Lookahead selective sampling for incomplete data

100%

Abdallah L., Shimshoni I.

International Journal of Applied Mathematics and Computer Science

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2016

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tom 26

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nr 4

871-884

EN

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.

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An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects

75%

Arciniegas-Alarcón S., García-Peña M., Krzanowski W. J., Santos Dias C. T. d.

Biometrical Letters

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2014

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tom 51

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nr 2

75-88

EN

A common problem in multi-environment trials arises when some genotypeby- environment combinations are missing. In Arciniegas-Alarcón et al. (2010) we outlined a method of data imputation to estimate the missing values, the computational algorithm for which was a mixture of regression and lower-rank approximation of a matrix based on its singular value decomposition (SVD). In the present paper we provide two extensions to this methodology, by including weights chosen by cross-validation and allowing multiple as well as simple imputation. The three methods are assessed and compared in a simulation study, using a complete set of real data in which values are deleted randomly at different rates. The quality of the imputations is evaluated using three measures: the Procrustes statistic, the squared correlation between matrices and the normalised root mean squared error between these estimates and the true observed values. None of the methods makes any distributional or structural assumptions, and all of them can be used for any pattern or mechanism of the missing values.

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Neuro-rough-fuzzy approach for regression modelling from missing data

75%

Simiński K.

International Journal of Applied Mathematics and Computer Science

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2012

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tom 22

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nr 2

461-476

EN

Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

1 Biometrical Letters

1 Abdallah L.

1 Arciniegas-Alarcón S.

1 García-Peña M.

1 Krzanowski W. J.

1 Santos Dias C. T. d.

1 Shimshoni I.

1 Simiński K.

1 2016

1 2014

1 2012

Lookahead selective sampling for incomplete data

An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects

Neuro-rough-fuzzy approach for regression modelling from missing data