A study of EEG feature complexity in epileptic seizure prediction

The purpose of this study is (1) to provide EEG feature complexity analysis in seizure prediction by inter-ictal and pre-ital data classification and, (2) to assess the between-subject variability of the considered features. In the past several decades, there has been a sustained interest in predicting epilepsy seizure using EEG data. Most methods classify features extracted from EEG, which they assume are characteristic of the presence of an epilepsy episode, for instance, by distinguishing a pre-ictal interval of data (which is in a given window just before the onset of a seizure) from inter-ictal (which is in preceding windows following the seizure). To evaluate the difficulty of this classification problem independently of the classification model, we investigate the complexity of an exhaustive list of 88 features using various complexity metrics, i.e., the Fisher discriminant ratio, the volume of overlap, and the individual feature efficiency. Complexity measurements on real and synthetic data testbeds reveal that that seizure prediction by pre-ictal/inter-ictal feature distinction is a problem of significant complexity. It shows that several features are clearly useful, without decidedly identifying an optimal set

Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect

Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, some properties can cause datasets to be problematic to classify. In order to evaluate a dataset a priori, data complexity metrics have been used extensively. They provide information regarding different intrinsic characteristics of the data, which serve to evaluate classifier compatibility and a course of action that improves performance. However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers' performance. In fact, class overlap, a very detrimental feature for the classification process (especially when imbalance among class labels is also present) is hard to assess. This research work focuses on revisiting complexity metrics based on data morphology. In accordance to their nature, the premise is that they provide both good estimates for class overlap, and great correlations with the classification performance. For that purpose, a novel family of metrics have been developed. Being based on ball coverage by classes, they are named after Overlap Number of Balls. Finally, some prospects for the adaptation of the former family of metrics to singular (more complex) problems are discussed.Comment: 23 pages, 9 figures, preprin

Domain of Competence of XCS Classifier System in Complexity Measurement Space

The XCS classifier system has recently shown a high degree of competence on a variety of data mining problems. But to what kind of problems XCS is well and poorly suited is seldom understood, especially for real-world classification problems. The major inconvenience has been attributed to the difficulty of determining the intrinsic characteristics of real-world classification problems. This paper investigates the domain of competence of XCS by means of a methodology that characterizes the complexity of a classi-fication problem by a set of geometrical descriptors. In a study of 392 classification problems along with their complexity characterization, we are able to identify difficult and easy domains for XCS. We focus on XCS with hyperrectangle codification, which has been predominantly used for real-attributed domains. The results show high correlations between XCS’s performance and measures of length of class boundaries, compactness of classes and non-linearities of decision boundaries. We also compare the relative performance of XCS with other traditional classifier schemes. Besides confirming the high degree of competence of XCS in these problems, we are able to relate the behavior of the different classifier schemes to the geometrical complexity of the problem. Moreover, the results highlight certain regions of the complexity measurement space where a classifier scheme excels, establishing a first step towards determining the best classifier scheme for a given classification problem. 1 Index Terms Learning classifier systems, geometrical complexity, genetic algorithms, pattern recognition, machin