A correlation-based binary particle swarm optimization method for feature selection in human activity recognition:

TL;DR: A comprehensive survey on the state-of-the-art works applying swarm intelligence to achieve feature selection in classification, with a focus on the representation and search mechanisms.

TL;DR: A set of time-domain features derived by considering the physical meaning of the acquired signals, including time, frequency, and time-frequency domain features widely used in literature, allows to better understand alterations of the biomechanical behavior in more complex situations.

TL;DR: In this article , the authors proposed an efficient smartphone-based human activity recognition system that extracts the most relevant features from the 3-axial accelerometer and gyroscope signal data and enhances the HAR system's classification accuracy without data loss using time-frequency domain features.

TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).

TL;DR: This chapter discusses Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks, and its application to Data Analysis and Graph Partitioning.

TL;DR: This is an index to the papers that appear in the Proceedings of the 29th International Conference on Machine Learning (ICML-12).

TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.