Population Synthesis via k-Nearest Neighbor Crossover Kernel

TL;DR

This paper introduces a novel kernel density estimator using crossover kernels, k-nearest neighbor restriction, and bagging, to improve population synthesis from limited samples for multi-agent simulations.

Contribution

It proposes a new high-dimensional population synthesis method with an optimization-free parameter selection and theoretical analysis, enhancing accuracy and computational efficiency.

Findings

Effective on real and synthetic datasets

Improves population synthesis accuracy

Reduces need for parameter tuning

Abstract

The recent development of multi-agent simulations brings about a need for population synthesis. It is a task of reconstructing the entire population from a sampling survey of limited size (1% or so), supplying the initial conditions from which simulations begin. This paper presents a new kernel density estimator for this task. Our method is an analogue of the classical Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the huge degree of freedom which is required to model high-dimensional nonlinearly correlated datasets: the crossover kernel, the k-nearest neighbor restriction of the kernel construction set and the bagging of kernels. The performance as a statistical estimator is examined through real and synthetic datasets. We provide an "optimization-free" parameter selection rule for our method, a theory of how our method works and a computational cost analysis. To demonstrate the usefulness as a population synthesizer, our method is applied to a household synthesis task for an urban micro-simulator.