Evolutionary Algorithms and Rough Sets-Based Hybrid Approach to Classificatory Decomposition of Cortical Evoked Potentials

Abstract

Abstract. This paper presents a novel approach to decomposition and classification of rat's cortical evoked potentials (EPs). The decomposition is based on learning of a sparse set of basis functions using Evolutionary Algorithms (EAs). The basis functions are generated in a potentially overcomplete dictionary of the EP components according to a probabilistic model of the data. Compared to the traditional, statistical signal decomposition techniques, this allows for a number of basis functions greater than the dimensionality of the input signals, which can be of a great advantage. However, there arises an issue of selecting the most significant components from the possibly overcomplete collection. This is especially important in classification problems performed on the decomposed representation of the data, where only those components that provide a substantial discernibility between EPs of different groups are relevant. In this paper, we propose an approach based on the Rough Set theory's (RS) feature selection mechanisms to deal with this problem. We design an EA and RS-based hybrid system capable of signal decomposition and, based on a reduced component set, signal classification.

Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets

“…Some examples of applications of CD can be found in. [21][22][23][24] In this article, we propose a hybridization of multi-objective evolutionary algorithms (MOEA) and rough sets (RS) to perform decomposition of two dimensional trajectories in light of the underlying classification problem itself (i.e., suspicious vs. usual trajectories). The idea is to look for basis functions whose coefficients allow for an accurate classification while preserving the reconstruction to the best possible extent.…”

Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials

“…Classificatory decomposition is a general term that describes our research study that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." The description of previous stages of the study and some examples of applications can be found in [2,3,4]. Currently, we are investigating a hybridization of multi-objective evolutionary algorithms (MOEA) and rough sets (RS) to perform decomposition in the light of the classification problem itself.…”

Multi-objective evolutionary algorithms and rough sets for decomposition and analysis of cortical evoked potentials

“…In the first attempt to incorporate classification accuracy into signal decomposition, a rough sets-based search for reducts was applied in post-processing of the discovered components [8]. This 2-stage approach allowed for selection of the most important components in the light of the classification task dealt with (a 2-category classification problem of evoked potentials), but did not escape the limitations of the decomposition process itself.…”