Wyniki wyszukiwania - Biblioteka Nauki

1

Clustering with Missing Values

100%

Simiński K.

Fundamenta Informaticae

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2013

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tom Vol. 123, nr 3

331--350

EN

The paper presents the clustering algorithm for data with missing values. In this approach both marginalisation and imputation are applied. The result of the clustering is the type-2 fuzzy set / rough fuzzy set. This approach enables the distinction between original and imputed data. The method can be applied to the data sets with all attributes lacking some values. The paper is accompanied by the numerical examples of clustering of synthetic and real-life data sets.

2

Some Remarks on the Data Imputation Using “missForest” Method

80%

Misztal M. , University of Lodz D. o. S. M.

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tom 285

EN

Missing data are quite common in practical applications of statistical methods and imputation is a general statistical method for the analysis of incomplete data sets. Stekhoven and Bühlmann (2012) proposed an iterative imputation method (called “missForest”) based on Random Forests (Breiman 2001) to cope with missing values. In the paper a short description of “missForest” is presented and some selected missing data techniques are compared with “missForest” by artificially simulating different proportions and mechanisms of missing data using complete data sets from the UCI repository of machine learning databases.

PL

W pracy Stekhovena i Bühlmanna (2012) zaproponowano nową iteracyjną metodę imputacji (nazwaną „missForest”) opartą na metodzie Random Forests Breimana (2001). W niniejszym artykule omówiono metodę „missForest” i porównano kilka wybranych technik postępowania w sytuacji występowania braków danych z metodą „missForest”. W tym celu wykorzystano podejście symulacyjne generując różne proporcje i mechanizmy powstawania braków danych w zbiorach danych pochodzących głównie z repozytorium baz danych na Uniwersytecie Kalifornijskim w Irvine.

3

Methods for imputation of missing values and their influence on the results of segmentation research

80%

Gąsior M. , Skowron Ł.

Econometrics. Ekonometria. Advances in Applied Data Analytics

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2016

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nr 4 (54)

61-71

EN

The lack of answers is a common problem in all types of research, especially in the field of social sciences. Hence a number of solutions were developed, including the analysis of complete cases or imputations that supplement the missing value with a value calculated according to different algorithms. This paper evaluates the influence of the adopted method for the supplementation of missing answers regarding the result of segmentation conducted with the use of cluster analysis. In order to achieve this we used a set of data from an actual consumer research in which the cases with missing values were deleted or supplemented with the use of various methods. Cluster analyses were then performed on those sets of data, both with the assumption of ordinal and ratio level of measurement, and then the grouping quality, as expressed by different indicators, was evaluated. This research proved the advantage of imputation over the analysis of complete cases, it also proved the validity of using more complex approaches than the simple supplementation with an average or median value.

4

Imputation of Missing Data Using R Package

80%

Misztal M. , Uniwersytet Łódzki; Wydział Ekonomiczno-Socjologiczny; Instytut Statystyki i Demografii

Acta Universitatis Lodziensis. Folia Oeconomica

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2012

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tom 269

EN

Missing data are quite common in practical applications of statistical methods. Imputation is general statistical method for the analysis of incomplete data sets. The goal of the paper is to review selected imputation techniques. Special attention is paid to methods implemented in some packages working in the R environment. An example is presented to show how to handle missing values using a few procedures of single and multiple imputation implemented in R.

5

Lookahead selective sampling for incomplete data

80%

Abdallah L. , Shimshoni I.

International Journal of Applied Mathematics and Computer Science

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2016

|

tom 26

|

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.

6

Jackknife winsorized variance estimator under imputed data

70%

Sohil F. , Sohail M. U. , Shabbir J. , Gupta S.

Statistics in Transition new series

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2022

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tom 23

|

nr 2

17-32

EN

In the present study, we consider the problem of missing and extreme values for the estimation of population variance. The presence of extreme values either in the study variable, or the auxiliary variable, or in both of them, can adversely affect the performance of the estimation procedure. We consider three different situations for the presence of extreme values and also consider jackknife variance estimators for the population variance by handling these extreme values under stratified random sampling. Bootstrap technique ABB is carried out to understand the relative relationship more precisely.

7

Lookahead selective sampling for incomplete data

60%

Abdallah L. , Shimshoni I.

International Journal of Applied Mathematics and Computer Science

|

2016

|

tom Vol. 26, no. 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.

8

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

60%

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.

9

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

60%

Simiński K.

International Journal of Applied Mathematics and Computer Science

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2012

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tom 22

|

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.

10

Impact of missing precipitation values on hydrological model output: a case study from the Eddleston Water catchment, Scotland

51%

Ruman S. , Krpec P. , Rusnok P. , Black A. R. , Trizna M. , Ball T.

Acta Geophysica

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2020

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tom Vol. 68, no. 2

565--576

EN

A hydrological model was applied to select the best inflling method of missing precipitation (1) and to assess the impact of the length of deleted and flled precipitation data (2). The model was calibrated and validated using the hourly observed discharges from two gauges located in the outlet of the catchment (62.34 km2 ) and in the inner sub-catchment (2.05 km2 ). Precipitation from four gauges was spatially interpolated over the overall catchment, while the sub-catchment used the precipitation from one gauge. Four scenarios of diferent lengths of deletion within three high-intensity events were established in the data of this gauge. Three inflling methods were applied and compared: substitution, linear regression and inverse distance weighting (IDW). Substitution showed the best results, followed by linear regression and IDW in both scales. Differences between methods were signifcant only in 8.3% and 19.4% of all cases (sub-catchment and catchment, respectively). The impact of length was assessed using the substitution only and by comparing diferences in discharges and performance statistics caused by four scenarios. Higher diferences in discharges were found on the catchment scale compared to the inner sub-catchment and were insignifcant for all events and scenarios. The hypothesis that a longer length of deleted and flled data would lead to a greater error in discharges was wrong for 11.1% and 16.7% of all cases (sub-catchment and catchment, respectively). In several cases (33.4% sub-catchment, 27.1% catchment), the model produced better results using the time series with flled gaps compared to the confguration with observed data.

11

Computer-Intensive Methods in Traffic Safety Research

41%

Harold S.

International Journal of Occupational Safety and Ergonomics

|

2002

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tom Vol. 8, No. 3

353--363

EN

The analysis of traffic safety data archives has improved markedly with the development of procedures that are heavily dependent upon computers. Three such procedures are described here. The first procedure involves using computers to assist in the identification and correction of invalid data. The second procedure makes greater computational demands, and involves using computerized algorithms to fill in the ‘‘gaps’’ that typically occur in archival data when information regarding key variables is not available. The third and most computer-intensive procedure involves using data mining techniques to search archives for interesting and important relationships between variables. These procedures are illustrated using examples from data archives that describe the characteristics of traffic accidents in the USA and Australia.

12

Stability of Optimal Parameters for Classifier Based on Simple Granules of Knowledge

41%

Artiemjew P.

Technical Sciences / University of Warmia and Mazury in Olsztyn

|

2011

|

tom nr 14(1)

57-69

EN

Searching for optimal parameters of a classifier based on simple granules of knowledge investigated recently by the author (ARTIEMJEW 2010) raises a question about stability of optimal parameters. In this article, we will check dependence of stability of the optimal radius of granulation on random damage of decision system. The results of experiments show the dependence of stability on size of damage and strategies of treating missing values. This kind of research aims at finding methods of protecting decision systems which are vulnerable to damage against decreasing their classification effectiveness, which means preserving classifying possibilities similar to undamaged decision systems.

PL

Przeprowadzone w ostatnim czasie badania (ARTIEMJEW 2010) zmierzające do wyszukiwania optymalnych parametrów klasyfikacji modułów decyzyjnych opartych na prostych granulach wiedzy zrodziły pytanie o stabilność optymalnych parametrów klasyfikacji. W pracy sprawdzono zależność stabilności optymalnych promieni granulacji od losowego uszkadzania systemu decyzyjnego. Wyniki badań wskazały jednoznacznie, że istnieje zależność między stabilnością a wielkością uszkodzenia i strategiami traktowania wartości uszkodzonych. Tego typu badania mają na celu szukanie metod zabezpieczania systemów decyzyjnych, które są podatne na uszkodzenia, przed zmniejszaniem ich efektywności klasyfikacyjnej. Celem było zachowanie możliwości klasyfikacyjnych zbliżonych do efektywności nieuszkodzonych systemów decyzyjnych.

13

Child well being estimation as a multi-criteria decision problem

41%

Adamus E. , Klęsk P. , Kołodziejczyk J. , Korzeń M. , Piegat A. , Pluciński M.

Metody Informatyki Stosowanej

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2011

|

tom nr 4

279-286

EN

The aim of the article is to establish a list of capabilities with reference to children’s well being in Poland. This issue can be considered as a multicriterial classification problem where decision attributes classes are ordered due to the preference – criteria. We used a Dominance-based Rough Set Approach (DRSA) adapted to deal with missing values. Analysis was performed on data set collected in Zachodniopomorskie district as a result of survey conduction.

14

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

41%

Simiński K.

International Journal of Applied Mathematics and Computer Science

|

2012

|

tom Vol. 22, no. 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.