Using data complexity measures and an evolutionary cultural algorithm for gene selection in microarray data

Sarbazi-Azad, Saeed; Abadeh, Mohammad Saniee; Mowlaei, Mohammad Erfan

doi:10.1016/j.socl.2020.100007

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…For example, an immediate consideration is that both patients had the same type of seizures (Temporal), and the average seizure duration was very similar. These findings push us to perform further research, by calculating, for example, the similarities among patients through an analysis of complexity metrics [ 48 , 49 ]. Moreover, the performance of the model on other patients was highly influenced by the feature-selection phase, which was conducted on patient No.17, so it was very patient specific.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection

Zazzaro

Pavone²

2022

Biomedicines

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Background: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive rates. The aim of this study is to evaluate the performance of a seizure detection method using EEG signals by investigating its performance in correctly identifying seizures and in minimizing false alarms and to determine if it is generalizable to different patients. Methods: We tested the method on about two hours of preictal/ictal and about ten hours of interictal EEG recordings of one patient from the Freiburg Seizure Prediction EEG database using machine learning techniques for data mining. Then, we tested the obtained model on six other patients of the same database. Results: The method achieved very high performance in detecting seizures (close to 100% of correctly classified positive elements) with a very low false-positive rate when tested on one patient. Furthermore, the model portability or transfer analysis revealed that the method achieved good performance in one out of six patients from the same dataset. Conclusions: This result suggests a strategy to discover clusters of similar patients, for which it would be possible to train a general-purpose model for seizure detection.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection

Zazzaro

Pavone²

2022

Biomedicines

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“…This complexity information has proved to be really useful for the posterior classification process. Complexity measures have been successfully implemented for a wide range of purposes like feature selection [27,22], imbalanced problems [4,33], label noise [13], Big Data [21], classifier recommendation [30,6], etc.…”

Section: State-of-the-artmentioning

confidence: 99%

Dynamic Disagreeing Neighbors: A complexity measure for instance selection

Aceña,

Lancho,

de Diego

et al. 2024

Preprint

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Within the scope of Machine Learning (ML), Instance Selection (IS) is a sampling process that consists of filtering noise and removing redundant data. In classification problems, IS implies a compromise between maximizing performance and reducing the sample size used for training. Complexity measures provide relevant information about the difficulty of classifying instances, which makes them appropriate for IS since they can capture, for example, noisy or borderline points. This paper introduces the complexity measure DDN, defined at three levels: instance, class, and dataset. The Dynamic Disagreeing Neighbors (DDN) of an instance is defined as the percentage of its nearest neighbors that belong to other classes. The DDN is based on the Nearest Centroid Neighbors (NCN) neighborhood computation, which is dynamically adjusted to the data distribution. In addition, the distance of each neighbor is taken into account so that those farther away are less influential and those closer are more influential for the instance complexity. The validity of the proposal is evaluated through a series of experiments where it is compared with the widely known k-Disagreeing Neighbors (kDN) in terms of stability, correlation with classification error, and performance in IS. The DDN has shown competitive, stable, and robust results throughout the experiments, generally improving on those obtained with the alternative.

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Can Complexity Measures and Instance Hardness Measures Reflect the Actual Complexity of Microarray Data?

Al Hosni,

Starkey

2024

Machine Learning, Optimization, and Data Science

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Using data complexity measures and an evolutionary cultural algorithm for gene selection in microarray data

Cited by 7 publications

References 32 publications

Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection

Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection

Dynamic Disagreeing Neighbors: A complexity measure for instance selection

Can Complexity Measures and Instance Hardness Measures Reflect the Actual Complexity of Microarray Data?

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