A machine learning tool for interpreting differences in cognition using brain features

Azevedo, Tiago; Passamonti, Luca; Lió, Píetro; Toschi, Nicola

doi:10.1101/558403

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The works in [34]- [36] just focuses on the model explanations for computer vision, and the works in [37], [38] use LIME to give the explanations in the fields of natural language processing and acoustic analysis. The works in [39], [40] use SHAP to improve the transparency of the models in the field of biology. Most of these works do not directly design or use for IDSs.…”

Section: Related Workmentioning

confidence: 99%

An Explainable Machine Learning Framework for Intrusion Detection Systems

et al. 2020

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In recent years, machine learning-based intrusion detection systems (IDSs) have proven to be effective; especially, deep neural networks improve the detection rates of intrusion detection models. However, as models become more and more complex, people can hardly get the explanations behind their decisions. At the same time, most of the works about model interpretation focuses on other fields like computer vision, natural language processing, and biology. This leads to the fact that in practical use, cybersecurity experts can hardly optimize their decisions according to the judgments of the model. To solve these issues, a framework is proposed in this paper to give an explanation for IDSs. This framework uses SHapley Additive exPlanations (SHAP), and combines local and global explanations to improve the interpretation of IDSs. The local explanations give the reasons why the model makes certain decisions on the specific input. The global explanations give the important features extracted from IDSs, present the relationships between the feature values and different types of attacks. At the same time, the interpretations between two different classifiers, one-vs-all classifier and multiclass classifier, are compared. NSL-KDD dataset is used to test the feasibility of the framework. The framework proposed in this paper leads to improve the transparency of any IDS, and helps the cybersecurity staff have a better understanding of IDSs' judgments. Furthermore, the different interpretations between different kinds of classifiers can also help security experts better design the structures of the IDSs. More importantly, this work is unique in the intrusion detection field, presenting the first use of the SHAP method to give explanations for IDSs.

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Section: Related Workmentioning

confidence: 99%

An Explainable Machine Learning Framework for Intrusion Detection Systems

et al. 2020

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“…Van Putten et al [8] used a deep convolutional neural network to predict sex from brain rhythms obtained in EEG (electroencephalogram) data and achieved an accuracy of 81%. Azevedo et al [9] applied machine learning approach for predicting individual differences in cognitive functioning by using features derived from brain surface-based morphometry and cortical myelin estimates. They reduced the number of features into 23 sets using Factor Analysis, out of which they extracted nine factors that represented 70% of cumulative variance using Principal Component Analysis.…”

Section: Introductionmentioning

confidence: 99%

Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females

et al. 2021

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We analysed features relevant for differentiation between males and females based on the data available from the Human Connectome Project (HCP) S1200 dataset. We used 354 features containing cognitive and emotional measures as well as measures derived from task functional magnetic resonance imaging (MRI) and structural brain MRI. The paper presents a thorough analysis of this extensive set of features using a machine learning approach with a goal of identifying features that have the ability to differentiate between males and females. We used two state of the art classification algorithms with different properties: support vector machine (SVM) and random forest classifier (RFC). For each classifier the hyperparameters were obtained and classifiers were optimized using nested cross validation and grid search. This resulted in the classification performance of 91% and 89% accuracy using SVM and RFC, respectively. Using SHAP (SHapley Additive exPlanations) method we obtained relevance of features as indicators of sex differences and identified features with high discriminative power for sex classification. The majority of top features were brain morphological measures, and only a small proportion were features related to cognitive performance. Our results demonstrate the importance and advantages of using a machine learning approach when analysing sex differences.

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A machine learning tool for interpreting differences in cognition using brain features

Cited by 2 publications

References 11 publications

An Explainable Machine Learning Framework for Intrusion Detection Systems

An Explainable Machine Learning Framework for Intrusion Detection Systems

Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females

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