Comparative evaluation of feature reduction methods for drug response prediction by Farzaneh Firoozbakht, Behnam Yousefi, Olga Tsoy, Jan Baumbach, Benno Schwikowski

“…This study presents the first comparative evaluation of nine different knowledge-based and data-driven feature reduction methods on cell line and tumor data. …”
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Learning Feature Fusion in Deep Learning-Based Object Detector by Ehtesham Hassan, Yasser Khalil, Imtiaz Ahmad

“…The present work shows a qualitative approach to identify the best layer for fusion and design steps for feeding in the additional feature sets in convolutional network-based detectors.…”
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Comparison of Various Feature Extractors and Classifiers in Wood Defect Detection by Kenan Kiliç, Kazım Kiliç, İbrahim Alper Doğru, Uğur Özcan

“…The results obtained are presented comparatively in terms of each feature and classifier. …”
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IMPACT ASSESSMENT OF IMAGE FEATURE EXTRACTORS ON THE PERFORMANCE OF SLAM SYSTEMS by Taihú Pire, Thomas Fischer, Jan Faigl

“…The evaluation was performed with a standard dataset with ground-truth information and six feature detectors and four descriptors. The presented results indicate that the combination of the GFTT detector and the BRIEF descriptor provides the best trade-off between the localization precision and computational requirements among the evaluated combinations of the detectors and descriptors.…”
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Method of anti-confusion texture feature descriptor for malware images by Yashu LIU, Zhihai WANG, Hanbing YAN, Yueran HOU, Yukun LAI

“…It is a new method that uses image processing and machine learning algorithms to classify malware samples in malware visualization field.The texture feature description method has great influence on the result.To solve this problem,a new method was presented that joints global feature of GIST with local features of LBP or dense SIFT in order to construct combinative descriptors of malware gray-scale images.Using those descriptors,the malware classification performance was greatly improved in contrast to traditional method,especially for those samples have higher similarity in the different families,or those have lower similarity in the same family.A lot of experiments show that new method is much more effective and general than traditional method.On the confusing dataset,the accuracy rate of classification has been greatly improved.…”
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Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

“…SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.…”
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Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

“…SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.…”
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Multi-Scale Feature Fusion Enhancement for Underwater Object Detection by Zhanhao Xiao, Zhenpeng Li, Huihui Li, Mengting Li, Xiaoyong Liu, Yinying Kong

“…Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. …”
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The maximum residual block Kaczmarz algorithm based on feature selection by Ran-Ran Li, Hao Liu

“…Then, we put forward the feature selection algorithm based on Lasso, and further presented a maximum residual block Kaczmarz algorithm based on feature selection. …”
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Deep Contextual Structure and Semantic Feature Enhancement Stereo Network by Guowei An, Yaonan Wang, Kai Zeng, Qing Zhu, Xiaofang Yuan, Yang Mo

“…To address the blur in thin structure regions and the dilation in depth discontinuity regions, the contextual structure enhancing module is proposed to enhance the extraction ability for local contextual features of the feature extraction network. To reduce the matching ambiguity in large textureless regions, the semantic feature enhancing module is proposed to enhance the aggregation ability for semantic features of the cost aggregation network. …”
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Insights into Galaxy Evolution from Interpretable Sparse Feature Networks by John F. Wu

“…To address this lack of interpretability, we present a novel neural network architecture called a Sparse Feature Network (SFNet). …”
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Hybrid feature learning framework for the classification of encrypted network traffic by S. Ramraj, G. Usha

“…Additionally, various feature learning frameworks based on deep learning, such as DNN, Autoencoder and PCA, are compared. …”
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FeatureForest: the power of foundation models, the usability of random forests by Mehdi Seifi, Damian Dalle Nogare, Juan Manuel Battagliotti, Vera Galinova, Ananya Kedige Rao, Pierre-Henri Jouneau, Anwai Archit, AI4Life Horizon Europe Programme Consortium, Constantin Pape, Johan Decelle, Florian Jug, Joran Deschamps

“…They, however, require manual prompting for each object or tedious post-processing to selectively segment these objects. Here, we present FeatureForest, a method that leverages the feature embeddings of large foundation models to train a random forest classifier, thereby providing users with a rapid way of semantically segmenting complex images using only a few labeling strokes. …”
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Feature Point Extraction from the Local Frequency Map of an Image by Jesmin Khan, Sharif Bhuiyan, Reza Adhami

“…We present simulation results of the detection of feature points from image utilizing the suggested technique and compare the proposed method with five existing approaches that yield good results. …”
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Scene categorization by Hessian-regularized active perceptual feature selection by Junwu Zhou, Fuji Ren

“…Abstract Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. …”
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Weak Preprocessing Iris Feature Matching Based on Bipartite Graph by Jin Zhang, Kangwei Wang, Rongrong Shi, Feng Xie, Qinghe Zheng, Ruizhe Zhang, Cheng Wu, Yiming Wang

“…Traditional methods, such as the Daugman algorithm typically normalize the annular iris region into a rectangular format during the preprocessing stage, followed by feature extraction and matching. However, these preprocessing steps often introduce distortions and struggle to adapt to multiresolution images, leading to inaccurate feature encoding. …”
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On-line Identification Method of Pantograph Anomaly Based on Feature Analysis by WANG Junping, MAO Huihua, SHEN Yunbo, LI Miaocheng, CHEN Xin'an

“…Therefore, this paper proposes an on-line recognition method based on feature analysis and independent of the number of fault samples. …”
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Emotion on the edge: An evaluation of feature representations and machine learning models by James Thomas Black, Muhammad Zeeshan Shakir

“…This paper presents a comprehensive analysis of textual emotion classification, employing a tweet-based dataset to classify emotions such as surprise, love, fear, anger, sadness, and joy. …”
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GlacioTools: Streamlining Glacier Feature Monitoring and Reporting in QGIS by S. Fajardo Turner, F. Gaspari, F. Migliaccio

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FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking by Hang Liu, Detian Huang, Mingxin Lin

“…To address this issue, we propose FETrack, a feature-enhanced transformer-based network for visual object tracking. …”
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