The Ensemble and Model Comparison Approaches for Big Data Analytics in Social Sciences

Big data analytics are prevalent in fields like business, engineering, public health, and the physical sciences, but social scientists are slower than their peers in other fields in adopting this new methodology. One major reason for this is that traditional statistical procedures are typically not suitable for the analysis of large and complex data sets. Although data mining techniques could alleviate this problem, it is often unclear to social science researchers which option is the most suitable one to a particular research problem. The main objective of this paper is to illustrate how the model comparison of two popular ensemble methods, namely, boosting and bagging, could yield an improved explanatory model. Accessed 993 times on https://pareonline.net from November 21, 2018 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Land use/land cover classification of fused Sentinel-1 and Sentinel-2 imageries using ensembles of Random Forests

The study explores the synergistic combination of Synthetic Aperture Radar (SAR) and Visible-Near Infrared-Short Wave Infrared (VNIR-SWIR) imageries for land use/land cover (LULC) classification. Image fusion, employing Bayesian fusion, merges SAR texture bands with VNIR-SWIR imageries. The research aims to investigate the impact of this fusion on LULC classification. Despite the popularity of random forests for supervised classification, their limitations, such as suboptimal performance with fewer features and accuracy stagnation, are addressed. To overcome these issues, ensembles of random forests (RFE) are created, introducing random rotations using the Forest-RC algorithm. Three rotation approaches: principal component analysis (PCA), sparse random rotation (SRP) matrix, and complete random rotation (CRP) matrix are employed. Sentinel-1 SAR data and Sentinel-2 VNIR-SWIR data from the IIT-Kanpur region constitute the training datasets, including SAR, SAR with texture, VNIR-SWIR, VNIR-SWIR with texture, and fused VNIR-SWIR with texture. The study evaluates classifier efficacy, explores the impact of SAR and VNIR-SWIR fusion on classification, and significantly enhances the execution speed of Bayesian fusion code. The SRP-based RFE outperforms other ensembles for the first two datasets, yielding average overall kappa values of 61.80% and 68.18%, while the CRP-based RFE excels for the last three datasets with average overall kappa values of 95.99%, 96.93%, and 96.30%. The fourth dataset achieves the highest overall kappa of 96.93%. Furthermore, incorporating texture with SAR bands results in a maximum overall kappa increment of 10.00%, while adding texture to VNIR-SWIR bands yields a maximum increment of approximately 3.45%.Comment: Thesis for Master of Technology. Created: July 2018. Total pages 12

Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft

Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline. Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning. This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand. Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood. For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations. High density seagrass was distinct from sediment, but classification error arose for sparse seagrass. Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale. The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution. The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application

A CRITICAL EXPLORATION OF THE POTENTIAL UTILITY OF RULE INDUCTION DATA MINING METHODS TO “ORTHODOX” EDUCATION RESEARCH

Despite some theoretical promise, it is unclear whether rule induction data mining approaches (e.g., classification trees and association rules) add methodological value to "orthodox" education research, i.e., research unrelated to computer-based education. To better understand whether and how rule induction methods could be useful to education researchers, I explored whether they, relative to regression approaches, (1) improve classification accuracy, and/or (2) offer new avenues of explanation. Additionally, I aimed to illustrate a practical and principled way to use the various rule induction approaches so researchers can more easily choose to use it. To these ends, I conducted an extended literature review on rule induction methods, and re-analyzed two regression studies (Byrnes & Miller, 2007; Thomas, 2006) on the National Educational Longitudinal Study of 1988 using ten rule induction approaches. Data mining happened in two rounds for each study: first, by using only the predictors used in the original study, and second by using all reasonable and available predictors. I compared results across methods and rounds to better understand whether, how, and why the rule induction may provide additional insights. I found that while rule induction approaches can be labor intensive and not necessarily more predictive than regression, they can provide unique descriptions of the sample that shows at-a-glance, how key predictors relate to each other and to the outcome. They can also help identify relationships between variables that held for some subgroups but not others. For example: (i) rulesets induced from Byrnes and Miller's dataset suggested that Algebra 2 and math self-concept were positively related to 12th grade math scores, but only for those who were higher achieving in 8th grade math; (ii) association rules mined from Thomas' dataset suggested that factors such as school safety and honors program participation were more strongly associated with 12th grade achievement for lower income and students with lower parental education. Thus, when relationships between the predictors and outcome may not be uniform across the population, rule induction can provide more information than regression in exploring those relationships. Lessons learned and recommendations on how to apply rule induction approaches are also discussed