Browsing by Author "Kumar, Rajeev"

Now showing 1 - 2 of 2

Access status: Open Access ,
Generalizing clustering inferences with ml augmentation of ordinal survey data
(Wydawnictwa AGH, 2024) Kumar, Bhupendera; Kumar, Rajeev
In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation and unification. Data augmentation techniques have proven effective in enhancing models’ performance by expanding the dataset’s size. We employ ML data augmentation, unification, and clustering techniques. First, we augment the limited survey data size using data augmentation technique(s). Second, we carry out data unification, followed by clustering for inferencing. We took two benchmark survey datasets to demonstrate the effectiveness of augmentation and unification. The first dataset contains information on aspiring student entrepreneurs’ characteristics, while the second dataset comprises survey data related to breast cancer. We compare the inferences drawn from the original survey data with those derived from the transformed data using the proposed scheme. The results of this study indicate that the machine learning approach, data augmentation with the unification of data followed by clustering, can be beneficial for generalizing the inferences drawn from the survey data.
Access status: Open Access ,
Transformation and classification of ordinal survey data
(Wydawnictwa AGH, 2023) Sadh, Roopam; Kumar, Rajeev
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.