Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data

Szenkovits, Annamária; Meszlényi, Regina; Búza, Krisztián; Gaskó, Noémi; Lung, Rodica Ioana; Suciu, Mihai Alexandru

doi:10.1007/978-3-319-67588-6_10

Cited by 21 publications

(12 citation statements)

References 43 publications

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“…As a part of future work, one can: (i) consider further information regarding IT ticket complexity prediction, such as the number of tasks and configuration items per ticket; (ii) test other application cases in the IT ticket area and beyond, i.e., further explore the potential of linguistic features; (iii) as we showed that selecting an appropriate subset of linguistic features can considerably improve the performance of classifiers, one may conduct further experiments with more advanced feature selection techniques [100].…”

Section: Discussionmentioning

confidence: 99%

IT Ticket Classification: The Simpler, the Better

2020

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Recently, automatic classification of IT tickets has gained notable attention due to the increasing complexity of IT services deployed in enterprises. There are multiple discussions and no general opinion in the research and practitioners' community on the design of IT ticket classification tasks, specifically the choice of ticket text representation techniques and classification algorithms. Our study aims to investigate the core design elements of a typical IT ticket text classification pipeline. In particular, we compare the performance of TF-IDF and linguistic features-based text representations designed for ticket complexity prediction. We apply various classifiers, including kNN, its enhanced versions, decision trees, naïve Bayes, logistic regression, support vector machines, as well as semi-supervised techniques to predict the ticket class label of low, medium, or high complexity. Finally, we discuss the evaluation results and their practical implications. As our study shows, linguistic representation not only proves to be highly explainable but also demonstrates a substantial prediction quality increase over TF-IDF. Furthermore, our experiments evidence the importance of feature selection. We indicate that even simple algorithms can deliver high-quality prediction when using appropriate linguistic features.

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Section: Discussionmentioning

confidence: 99%

IT Ticket Classification: The Simpler, the Better

2020

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“…It typi-cally causes the curse of dimensionality which generate the classification accuracy with false enhancement [28]. To address this issue, numerous feature selection methods have been proposed such as t-test, least absolute shrinkage and selection operator (LASSO) [29], genetic algorithm (GA) [30] and so on. In this work, we only adopt the simple feature selection method, i.e.…”

Section: Fbn-based Disease Classificationmentioning

confidence: 99%

Estimating Functional Brain Network with Low-Rank Structure via Matrix Factorization for MCI/ASD Identification

Du¹,

Zhang²

2021

JAMP

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Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been proposed currently, including the low-order Pearson's correlation (PC) and sparse representation (SR), as well as the high-order functional connection (HoFC). However, most existing methods usually ignore the information of topological structures of FBN, such as low-rank structure which can reduce the noise and improve modularity to enhance the stability of networks. In this paper, we propose a novel method for improving the estimated FBNs utilizing matrix factorization (MF). More specifically, we firstly construct FBNs based on three traditional methods, including PC, SR, and HoFC. Then, we reduce the rank of these FBNs via MF model for estimating FBN with low-rank structure. Finally, to evaluate the effectiveness of the proposed method, experiments have been conducted to identify the subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) from norm controls (NCs) using the estimated FBNs. The results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate that the classification performances achieved by our proposed method are better than the selected baseline methods.

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“…For the purpose of this study, we employed the nearest neighbors ( K-NN) classifier to evaluate the candidate feature subset and as a reward function. We used the simple and efficient nearest neighbors classifier as it is well-understood in the literature and works surprisingly well in many situations [ 41 , 42 , 43 , 44 ]. Moreover, many other similar studies and comparison methods in literature, mentioned in Section 5.3 , have applied the nearest neighbors classifier and therefore, we considered it to be a better choice for the comparative analysis.…”

Section: Motifs (Monte Carlo Tree Search Based Feature Selection)mentioning

confidence: 99%

MOTiFS: Monte Carlo Tree Search Based Feature Selection

Chaudhry

Lee

2018

Entropy

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Given the increasing size and complexity of datasets needed to train machine learning algorithms, it is necessary to reduce the number of features required to achieve high classification accuracy. This paper presents a novel and efficient approach based on the Monte Carlo Tree Search (MCTS) to find the optimal feature subset through the feature space. The algorithm searches for the best feature subset by combining the benefits of tree search with random sampling. Starting from an empty node, the tree is incrementally built by adding nodes representing the inclusion or exclusion of the features in the feature space. Every iteration leads to a feature subset following the tree and default policies. The accuracy of the classifier on the feature subset is used as the reward and propagated backwards to update the tree. Finally, the subset with the highest reward is chosen as the best feature subset. The efficiency and effectiveness of the proposed method is validated by experimenting on many benchmark datasets. The results are also compared with significant methods in the literature, which demonstrates the superiority of the proposed method.

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Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data

Cited by 21 publications

References 43 publications

IT Ticket Classification: The Simpler, the Better

IT Ticket Classification: The Simpler, the Better

Estimating Functional Brain Network with Low-Rank Structure via Matrix Factorization for MCI/ASD Identification

MOTiFS: Monte Carlo Tree Search Based Feature Selection

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