Predictive Learning in the Presence of Heterogeneity and Limited Training Data

By Anuj Karpatne, Ankush Khandelwal, Shyam Boriah, Vipin Kumar. 2014

32 citations recorded by the source corpus.

A large number of real-world domains possess heterogeneity in their data, which implies that different partitions of the data show different relationships between explanatory and response variables. This increases the overall model complexity of predictive learning in the presence of heterogeneity. Additionally, a number of real-world domains lack sufficient training data, making the learning algorithm prone to over-fitting, especially when the model complexity is large. However, there often exists a structure among the data instances and their partitions which can be appropriately leveraged for reducing the model complexity along with addressing heterogeneity. In this paper, we present a framework for learning robust predictive models in real-world heterogeneous datasets which lack sufficient number of training samples. We demonstrate the usefulness of our framework in the domain of remote sensing for forest cover estimation. Through a series of comparative experiments with baseline approaches, we are able to show that our framework: (a) captures meaningful information about heterogeneity in the data, (b) improves prediction performance by addressing data heterogeneity, (c) is robust to over-fitting in the presence of limited training data, and (d) is robust to the choice of the number of partitions used for representing heterogeneity.

Subfields: Artificial Intelligence, Environmental Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Vipin Kumar
2. Anuj Karpatne
3. Shashi Shekhar
4. Charu C. Aggarwal
5. Arthur Zimek
6. Bamshad Mobasher

In Environmental Engineering

1. Vipin Kumar
2. Anuj Karpatne

Citations in the corpus

• Machine Learning for the Geosciences: Challenges and Opportunities (2017, background): “An example of a multi-task learning based approach for handling heterogeneity can be found in a recentwork in [40], where the learning”
• Monitoring Land-Cover Changes: A Machine-Learning Perspective (2016, methodology): “Comparing the performance of a multitask learning method [46] for forest-cover estimation in four states in Brazil with respect to a baseline approach of learning a single global model.”