Evolutionary Multi-Objective Feature Selection Algorithms on Multiple Smart Sustainable Community Indicator Datasets

Abstract

The conceptual fusion of smart city and sustainability indicators has inspired the emergence of the smart sustainable city (SSC). Given the early stage of development in this field, most SSC studies have been primarily theoretical. Notably, existing empirical studies have overlooked the crucial aspect of feature engineering in the context of SSC, despite its significance in advancing SSC initiatives. This paper introduces an approach advocating for feature subset selection to maximize prediction accuracy and minimize computational time across diverse SSC indicators encompassing socio-cultural, economic, environmental, and governance categories. The study systematically collected multiple datasets on SSC indicators, covering various themes within the SSC framework. Employing six carefully chosen multiple-objective evolutionary feature selection algorithms, the research selected feature subsets. These subsets were then utilized in modeling algorithms to predict SSC indicators. The proposal enhanced prediction accuracy for life expectancy, online shopping intentions, energy consumption, air quality, water quality, and traffic flow for a smart and sustainable city by minimizing the subset features. The findings underscore the efficacy of feature subset selection in generating minimal features, thereby enhancing both prediction accuracy and computational efficiency in the realm of SSC indicators. For researchers aiming to develop sustainable systems for real-time data monitoring within SSC, the identified subset features offer a valuable resource, negating the necessity for extensive dataset collection. The provided SSC datasets are anticipated to serve as a catalyst, inspiring researchers to embark on empirical studies that explore SSC development from diverse perspectives, ultimately contributing to a more profound understanding of the SSC dynamics.