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1. Evolutionary Programming Based Deep Feature and Model Selection for Visual Data Classification

   https://doi.org/10.1109/MIPR49039.2020.00020  

   Tian, Haiman; Chen, Shu-Ching; Shyu, Mei-Ling (August 2020, 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR))

   null (Ed.)

   Abstract: Deep Learning (DL) has made significant changes to a large number of research areas in recent decades. For example, several astonishing Convolutional Neural Network (CNN) models have been built by researchers to fulfill image classification needs using large-scale visual datasets successfully. Transfer Learning (TL) makes use of those pre-trained models to ease the feature learning process for other target domains that contain a smaller amount of training data. Currently, there are numerous ways to utilize features generated by transfer learning. Pre-trained CNN models prepare mid-/high-level features to work for different targeting problem domains. In this paper, a DL feature and model selection framework based on evolutionary programming is proposed to solve the challenges in visual data classification. It automates the process of discovering and obtaining the most representative features generated by the pre-trained DL models for different classification tasks. 

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2. Time-Series Feature Selection for Solar Flare Forecasting

   https://doi.org/10.3390/universe10090373  

   Velanki, Yagnashree; Hosseinzadeh, Pouya; Boubrahimi, Soukaina Filali; Hamdi, Shah Muhammad (September 2024, Universe)

   Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple methods, our ensemble method provides a more comprehensive understanding of the importance of features. Our results show that the ensemble approach significantly improves classification performance, achieving values 0.15 higher in True Skill Statistic (TSS) values compared to individual feature selection methods. Additionally, our method offers valuable insights into the underlying physical processes of solar flares, leading to more effective space weather forecasting and enhanced mitigation strategies for communication, navigation, and power system disruptions. 

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3. Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework

   https://doi.org/10.1364/BOE.413666  

   Deligani, Roohollah_Jafari; Borgheai, Seyyed_Bahram; McLinden, John; Shahriari, Yalda (February 2021, Biomedical Optics Express)

   Multimodal data fusion is one of the current primary neuroimaging research directions to overcome the fundamental limitations of individual modalities by exploiting complementary information from different modalities. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are especially compelling modalities due to their potentially complementary features reflecting the electro-hemodynamic characteristics of neural responses. However, the current multimodal studies lack a comprehensive systematic approach to properly merge the complementary features from their multimodal data. Identifying a systematic approach to properly fuse EEG-fNIRS data and exploit their complementary potential is crucial in improving performance. This paper proposes a framework for classifying fused EEG-fNIRS data at the feature level, relying on a mutual information-based feature selection approach with respect to the complementarity between features. The goal is to optimize the complementarity, redundancy and relevance between multimodal features with respect to the class labels as belonging to a pathological condition or healthy control. Nine amyotrophic lateral sclerosis (ALS) patients and nine controls underwent multimodal data recording during a visuo-mental task. Multiple spectral and temporal features were extracted and fed to a feature selection algorithm followed by a classifier, which selected the optimized subset of features through a cross-validation process. The results demonstrated considerably improved hybrid classification performance compared to the individual modalities and compared to conventional classification without feature selection, suggesting a potential efficacy of our proposed framework for wider neuro-clinical applications. 

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4. Feature Selections for Phishing URLs Detection Using Combination of Multiple Feature Selection Methods

   Abulfaz Hajizada and Sharmin Jahan (June 2023, ACM ICMLC 2023 : ACM--2023 15th International Conference on Machine Learning and Computing (ICMLC 2023))

   Cite: Abulfaz Hajizada and Sharmin Jahan. 2023. Feature Selections for Phishing URLs Detection Using Combination of Multiple Feature Selection Methods. In 2023 15th International Conference on Machine Learning and Computing (ICMLC 2023), February 17–20, 2023, Zhuhai, China. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3587716.3587790 ABSTRACT In this internet era, we are very prone to fall under phishing attacks where attackers apply social engineering to persuade and manipulate the user. The core attack target is to steal users’ sensitive information or install malicious software to get control over users’ devices. Attackers use different approaches to persuade the user. However, one of the common approaches is sending a phishing URL to the user that looks legitimate and difficult to distinguish. Machine learning is a prominent approach used for phishing URLs detection. There are already some established machine learning models available for this purpose. However, the model’s performance depends on the appropriate selection of features during model building. In this paper, we combine multiple filter methods for feature selections in a procedural way that allows us to reduce a large number of feature list into a reduced number of the feature list. Then we finally apply the wrapper method to select the features for building our phishing detection model. The result shows that combining multiple feature selection methods improves the model’s detection accuracy. Moreover, since we apply the backward feature selection method as our wrapper method on the data set with a reduced number of features, the computational time for backward feature selection gets faster. 

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5. A novel sensitivity-based method for feature selection

   https://doi.org/10.1186/s40537-021-00515-w  

   Naik, Dayakar L.; kiran, Ravi (October 2021, Journal of Big Data)

   Abstract Sensitivity analysis is a popular feature selection approach employed to identify the important features in a dataset. In sensitivity analysis, each input feature is perturbed one-at-a-time and the response of the machine learning model is examined to determine the feature's rank. Note that the existing perturbation techniques may lead to inaccurate feature ranking due to their sensitivity to perturbation parameters. This study proposes a novel approach that involves the perturbation of input features using a complex-step. The implementation of complex-step perturbation in the framework of deep neural networks as a feature selection method is provided in this paper, and its efficacy in determining important features for real-world datasets is demonstrated. Furthermore, the filter-based feature selection methods are employed, and the results obtained from the proposed method are compared. While the results obtained for the classification task indicated that the proposed method outperformed other feature ranking methods, in the case of the regression task, it was found to perform more or less similar to that of other feature ranking methods. 

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