Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: ordinal data

Sortuj według:

Ogranicz wyniki do:

Transformation and classification of ordinal survey data

100%

Sadh R. , Kumar R.

Computer Science

2023

tom T. 24 (2)

205--224

Currently, machine learning is being significantly used in almost all of the research domains; however, its applicability in survey research is still in its infancy. In this paper, we attempt to highlight the applicability of machine learning in survey research while working on two different aspects in parallel. First, we introduce a pattern-based transformation method for ordinal survey data. Our purpose for developing such a transformation method is two-fold: our transformation facilitates the easy interpretation of ordinal survey data and provides convenience while applying standard machine-learning approaches; and second, we demonstrate the application of various classification techniques over real and transformed ordinal survey data and interpret their results in terms of their suitability in survey research. Our experimental results suggest that machine learning coupled with a pattern-recognition paradigm has tremendous scope in survey research.

Permutation Test for Comparing Structures on the Example of the High–Bay Warehouse

86%

Polko D.

Logistyka

2014

tom nr 4

4821--4828, CD6

In economics research also in logistic it is often important to compare structures. The issue of comparison multivariate structures based on data in contingency tables was analyzed. The permutation rank–based tests were applied in the multivariate ordinal data structure comparison. Permutation tests could be used in practice because of flexibility of the test statistic and minimal assumptions. In the case of Kendell’s tau statistic verification of hypothesis on similarity (stability) of the structures is equal to verify the hypothesis on independence of variables. The application of the proposed procedure on the example of the high–bay warehouse was illustrated.

W badaniach ekonomicznych również w logistyce często istotne jest porównywanie struktur. Analizowano zagadnienie dotyczące porównania wielowymiarowych struktur na podstawie danych w tablicach kontyngencji. Permutacyjny test oparty na rangach został zastosowany do porównywania wielowymiaro-wych, mierzonych na skali porządkowej danych. Testy permutacyjne mogą być wykorzystane w praktyce ze względu na dużą elastyczność statystyki testowej przy minimalnych założeniach. W przypadku statystyki tau Kendell’a weryfikacja hipotezy o podobieństwie (stabilności) struktur jest tożsama z weryfikacją hipotezy o niezależności zmiennych. Zastosowano proponowaną procedurę testową dla danych pochodzących z magazynu wysokiego składowania.

Generalizing clustering inferenceswith ml augmentation ofordinal survey data

86%

Kumar B. , Kumar R.

Computer Science

2024

tom T. 25 (1)

63--93

In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation andunification. Data augmentation techniques have proven effective in enhancingmodels’ performance by expanding the dataset’s size. We employ ML dataaugmentation, unification, and clustering techniques. First, we augment thelimitedsurvey data size using data augmentation technique(s). Second, wecarry out data unification, followed by clustering for inferencing. We took twobenchmark survey datasets to demonstrate the effectiveness of augmentationand unification. The first dataset contains information on aspiring studententrepreneurs’ characteristics, while the second dataset comprises survey datarelated to breast cancer. We compare the inferences drawn from the originalsurvey data with those derived from the transformed data using the proposedscheme. The results of this study indicate that the machine learning approach,data augmentation with the unification of data followed by clustering, can bebeneficial for generalizing the inferences drawn from the survey data.