Temat: imputation - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Influence of missing data imputation method on the classification accuracy of the medical data
Autorzy:: Orczyk, T.
Porwik, P.
Powiązania:: https://bibliotekanauki.pl/articles/334037.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: medical data analysis
missing data
data imputation
classification efficiency
analiza danych medycznych
brakujące dane
przypisanie danych
efektywność klasyfikacji
Opis:: Aim of this study is to show the dangers of filling missing data - particularly medical data. Because there are many dedicated medical expert systems and medical decision support systems, a special attention must be paid on the construction of classifiers. Medical data are almost never complete, and completion of the missing data requires a special care. The safest approach of dealing with missing data would be removing records with missing parameters and/or removing parameters that are missing in the records. Unfortunately reducing data set that is already very small is not always an option. Dangers coming out from data imputation are shown in the article, which presents the influence of selected missing data filling algorithms on the classification accuracy.
Źródło:: Journal of Medical Informatics & Technologies; 2013, 22; 111-116
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Classifiers accuracy improvement based on missing data imputation
Autorzy:: Jordanov, I.
Petrov, N.
Petrozziello, A.
Powiązania:: https://bibliotekanauki.pl/articles/91626.pdf
Data publikacji:: 2018
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: machine learning
missing data
model-based imputation
neural networks
random forests
support vector machine
radar signal classification
nauczanie maszynowe
brakujące dane
sieci neuronowe
maszyna wektorów nośnych
klasyfikacja sygnałów radarowych
Opis:: In this paper we investigate further and extend our previous work on radar signal identification and classification based on a data set which comprises continuous, discrete and categorical data that represent radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the most of the real world datasets, it also contains high percentage of missing values and to deal with this problem we investigate three imputation techniques: Multiple Imputation (MI); K-Nearest Neighbour Imputation (KNNI); and Bagged Tree Imputation (BTI). We apply these methods to data samples with up to 60% missingness, this way doubling the number of instances with complete values in the resulting dataset. The imputation models performance is assessed with Wilcoxon’s test for statistical significance and Cohen’s effect size metrics. To solve the classification task, we employ three intelligent approaches: Neural Networks (NN); Support Vector Machines (SVM); and Random Forests (RF). Subsequently, we critically analyse which imputation method influences most the classifiers’ performance, using a multiclass classification accuracy metric, based on the area under the ROC curves. We consider two superclasses (‘military’ and ‘civil’), each containing several ‘subclasses’, and introduce and propose two new metrics: inner class accuracy (IA); and outer class accuracy (OA), in addition to the overall classification accuracy (OCA) metric. We conclude that they can be used as complementary to the OCA when choosing the best classifier for the problem at hand.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 1; 31-48
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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