Mixture proportion estimation (MPE) is a fundamental tool for solving a number of weakly supervised learning problems – supervised learning problems where label information is noisy or missing. Previous work on MPE has established a universally consistent estimator. In this work we establish a rate of convergence for mixture proportion estimation under an appropriate distributional assumption, and argue that this rate of convergence is useful for analyzing weakly supervised learning algorithms that build on MPE. To illustrate this idea, we examine an algorithm for classification in the presence of noisy labels based on surrogate risk minimization, and show that the rate of convergence for MPE enables proof of the algorithm’s consistency. Finally, we provide a practical implementation of mixture proportion estimation and demonstrate its efficacy in classification with noisy labels.

A rate of convergence for mixture proportion estimation, with application to learning from noisy labels

The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

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The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

Globally, 21 percent of young women are married before their 18th birthday. Despite some progress in addressing child marriage, it remains a widespread practice, in particular in South Asia. While household predictors of child marriage have been studied extensively in the literature, the evidence base on macro-economic factors contributing to child marriage and models that predict where child marriage cases are most likely to occur remains limited. In this paper we aim to fill this gap and explore region-level indicators to predict the persistence of child marriage in four countries in South Asia, namely Bangladesh, India, Nepal and Pakistan. We apply machine learning techniques to child marriage data and develop a prediction model that relies largely on regional and local inputs such as droughts, floods, population growth and nightlight data to model the incidence of child marriages. We find that our gradient boosting model is able to identify a large proportion of the true child marriage cases and correctly classifies 77% of the true marriage cases, with a higher accuracy in Bangladesh (92% of the cases) and a lower accuracy in Nepal (70% of cases). In addition, all countries contain in their top 10 variables for classification nighttime light growth, a shock index of drought over the previous and the last two years and the regional level of education, suggesting that income shocks, regional economic activity and regional education levels play a significant role in predicting child marriage. Given the accuracy of the model to predict child marriage, our model is a valuable tool to support policy design in countries where household-level data remains limited.

Economic development, weather shocks and child marriage in South Asia: A machine learning approach

Healthcare is a sensitive sector, and addressing the class imbalance in the healthcare domain is a time-consuming task for machine learning-based systems due to the vast amount of data. This study looks into the impact of socioeconomic disparities on the healthcare data of diabetic patients to make accurate disease predictions.This study proposed a systematic approach of Closest Distance Ranking and Principal Component Analysis to deal with the unbalanced dataset. A typical machine learning technique was used to analyze the proposed approach. The data set of pregnant diabetic women is analysed for accurate detection.The results of the case are analysed using sensitivity, which demonstrates that the minority class's lack of information makes it impossible to forecast the results. On the other hand, the unbalanced dataset was treated using the proposed technique and evaluated with the machine learning algorithm which significantly increased the performance of the system.The performance of the machine learning-based system was significantly enhanced by the unbalanced dataset which was processed with the proposed technique and evaluated with the machine learning algorithm. For the first time, an unbalanced dataset was treated with a combination of Closest Distance Ranking and Principal Component Analysis. 

Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic prediction.

We use a debiased Machine Learning technique to explore causes behind infant malnutrition for households below-poverty-line in India and examine effectiveness of various government interventions al...

Infant malnutrition, clean-water access and government interventions in India: a machine learning approach towards causal inference

This article uses a nation-wide household survey data from India and identifies important predictors of neonatal and infant mortality using multiple machine learning (ML) techniques. The consensus ...

Early warning signs: targeting neonatal and infant mortality using machine learning

Essays in the Application of Machine Learning in Development Economics

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Dweepobotee Brahma

Papers

Early warning signs: targeting neonatal and infant mortality using machine learning

Essays in the Application of Machine Learning in Development Economics

The Effect of Inflation Targeting Policies on Dollarization-A Cross-Country Analysis

Infant malnutrition, clean-water access and government interventions in India: a machine learning approach towards causal inference