An Enhanced Tree Ensemble for Classification in the Presence of Extreme Class Imbalance by Samir K. Safi, Sheema Gul

“…The efficacy of the proposed method is assessed using twenty benchmark problems for binary classification with moderate to extreme class imbalance, comparing it against other well-known methods such as optimal tree ensemble (OTE), SMOTE random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi><mi>F</mi></mrow><mrow><mi>S</mi><mi>M</mi><mi>O</mi><mi>T</mi><mi>E</mi></mrow></msub></mrow></semantics></math></inline-formula>), oversampling random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow></msub></mrow></semantics></math></inline-formula>), under-sampling random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">U</mi><mi mathvariant="normal">S</mi></mrow></msub></mrow></semantics></math></inline-formula>), k-nearest neighbor (k-NN), support vector machine (SVM), tree, and artificial neural network (ANN). …”
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Soils and Fertilizers for Master Gardeners: The Soil Profile and Soil Classification by Amy L. Shober

“…SL-260, a 3-page illustrated fact sheet by Amy L. Shober, provides information about the characteristics and classification of soils as found in the landscape under natural conditions. …”
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Soils and Fertilizers for Master Gardeners: The Soil Profile and Soil Classification by Amy L. Shober

“…SL-260, a 3-page illustrated fact sheet by Amy L. Shober, provides information about the characteristics and classification of soils as found in the landscape under natural conditions. …”
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Subregion duality, wedge classification and no global symmetries in AdS/CFT by Ning Bao, Yikun Jiang, Joydeep Naskar

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Advanced phenotyping in tomato fruit classification through artificial intelligence by Sandra Eulália Santos Faria, Alcinei Místico Azevedo, Nayany Gomes Rabelo, Varlen Zeferino Anastácio, Valentina de Melo Maciel, Deltimara Viana Matos, Elias Barbosa Rodrigues, Phelipe Souza Amorim, Janete Ramos da Silva, Fernanda de Souza Santos

“…The performance of five architectures - VGG16, InceptionV3, ResNet50, EfficientNetB3, and InceptionResNetV2 was evaluated to identify and determine the most efficient one for this classification. …”
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Cross-ViT based benign and malignant classification of pulmonary nodules. by Qinfang Zhu, Liangyan Fei

“…The results show that the accuracy, precision, recall and F1 score of the proposed method are 0.3%, 0.11%, 4.52% and 3.03% higher than those of the average optimal method, respectively, and the performance of Cross-ViT network for benign and malignant classification is better than most classification methods.…”
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Transcriptome Classification Reveals Molecular Subgroups in Idiopathic Pulmonary Fibrosis by Yuxia Liu, Chang Xu, Wenxin Gao, Huaqiong Liu, Chenglong Li, Mingwei Chen

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Classification of coronal plane alignment of arthritic and healthy knees in Japan by Gai Kobayashi, Masahiro Hasegawa, Yohei Yamabe, Shine Tone, Yohei Naito, Akihiro Sudo

“…This study aimed to evaluate the CPAK classification of healthy and arthritic knees in Japan. …”
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Classification of Continuous Sky Brightness Data Using Random Forest by Rhorom Priyatikanto, Lidia Mayangsari, Rudi A. Prihandoko, Agustinus G. Admiranto

“…Among those features, 10 are considered to be the most important for the classification task. The model was trained to classify the data into six classes (1: peculiar data, 2: overcast, 3: cloudy, 4: clear, 5: moonlit-cloudy, and 6: moonlit-clear) and then tested to achieve high accuracy (92%) and scores (F-score = 84% and G-mean = 84%). …”
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Endoscopic landmarks corresponding to anatomical landmarks for esophageal subsite classification by Ryu Ishihara, Yasuhiro Tani, Yuki Okubo, Yuya Asada, Tomoya Ueda, Daiki Kitagawa, Takehiro Ninomiya, Atsuko Tamashiro, Shunsuke Yoshii, Satoki Shichijo, Takashi Kanesaka, Sachiko Yamamoto, Yoji Takeuchi, Koji Higashino, Noriya Uedo, Tomoki Michida

“…Abstract Objectives Individual treatment strategies for esophageal cancer have been investigated based on the anatomical subsite classification. Accurate subsite classification based on these anatomical landmarks is thus important. …”
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Robust Face Recognition Using Deep Learning and Ensemble Classification by Pavani Chitrapu, Mahesh Kumar Morampudi, Hemantha Kumar Kalluri

“…The approach is tested on datasets such as CASIA3D and 105PinsFace, which include variations in illumination conditions. …”
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Clinical Classification of Urethrocutaneous Fistulas Developing after Hypospadias Repair by Abhinav Singh, Malika Singh, Raghubir Singh

“…Background Clinical classification of the urethrocutaneous fistulas (UCFs) was designed to help the surgeons in (1) categorizing the fistulas, (2) selecting appropriate treatments, (3) keeping record at presentation and discharge, and (4) transferring information while referring a patient with recurrent fistula to a higher center. …”
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SLFCNet: an ultra-lightweight and efficient strawberry feature classification network by Wenchao Xu, Yangxu Wang, Jiahao Yang

“…Among these tasks, the classification of strawberries stands as a pivotal juncture. …”
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Low complexity radar signal classification based on spectrum shape by Liang YIN, Rui LIN, Xiaolei WANG, Yuliang YAO, Lin ZHOU, Yuan HE

“…In order to solve the problems of high computational complexity, low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition, a low complexity radar signal classification algorithm based on spectrum shape was proposed.Signal spectrum was normalized, feature parameters were extracted by spectrum sampling method, and then machine learning classification model was trained.The test results of the data generated by the radar signal source show that the classification accuracy of Barker code, Frank code, LFM code, BPSK, QPSK modulation and conventional radar signals is more than 90% (SNR≥3 dB).The algorithm has low computational complexity, can adapt to the change of signal parameters, and has good generalization.…”
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A NOVEL DATASET FOR VIETNAMESE NEW YEAR FOOD CLASSIFICATION by Duy Nguyen Vo, Van Tan Luu Ngo, Thi Phuong Vy Le, Nguyen Ngoc Huyen Van, Duc Anh Phuc Nguyen, Van Tuan Kiet Ngo, Thanh Thang Truong, Tan Tai Pham, Nhat Minh Dinh, Thai Ngoc Ho, Tan Tran Minh Khang Nguyen

“…We have experimented with classification using feature vectors from network architectures such as VGG16, Inception-v3, ResNet-50, Xception, and MobileNet-v2 to train support vector machines (SVM), meeting the dataset’s challenges and laying the groundwork for the development of many optimal methods in the future that promise scientific breakthroughs in the service and commercial industries. …”
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A NOVEL DATASET FOR VIETNAMESE NEW YEAR FOOD CLASSIFICATION by Duy Nguyen Vo, Van Tan Luu Ngo, Thi Phuong Vy Le, Nguyen Ngoc Huyen Van, Duc Anh Phuc Nguyen, Van Tuan Kiet Ngo, Thanh Thang Truong, Tan Tai Pham, Nhat Minh Dinh, Thai Ngoc Ho, Tan Tran Minh Khang Nguyen

“…We have experimented with classification using feature vectors from network architectures such as VGG16, Inception-v3, ResNet-50, Xception, and MobileNet-v2 to train support vector machines (SVM), meeting the dataset’s challenges and laying the groundwork for the development of many optimal methods in the future that promise scientific breakthroughs in the service and commercial industries. …”
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A NOVEL DATASET FOR VIETNAMESE NEW YEAR FOOD CLASSIFICATION by Duy Nguyen Vo, Van Tan Luu Ngo, Thi Phuong Vy Le, Nguyen Ngoc Huyen Van, Duc Anh Phuc Nguyen, Van Tuan Kiet Ngo, Thanh Thang Truong, Tan Tai Pham, Nhat Minh Dinh, Thai Ngoc Ho, Tan Tran Minh Khang Nguyen

“…We have experimented with classification using feature vectors from network architectures such as VGG16, Inception-v3, ResNet-50, Xception, and MobileNet-v2 to train support vector machines (SVMs), meeting the dataset’s challenges and laying the groundwork for the development of many optimal methods in the future that promise scientific breakthroughs in the service and commercial industries. …”
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Deep learning based stacking ensembles for tropical sorghum classification by Muhammad Aqil, Muhammad Azrai, Roy Efendi, Nining Nurini Andayani, Suwardi, Bunyamin Zainuddin, Suarni, Herawati, Andi Irma Damayanti, Muhammad Jihad, Syafruddin, Ramlah Arief, Paesal, Yustisia, Rahman

“…Multi-dimensional scaling (MDS) analysis of misclassifications indicated that errors were concentrated among the Kawali, Numbu, and Suri 3 Agritan varieties. Overall, the SqueezeNet-LR stacking model outperformed the others, demonstrating its superiority in seed classification. …”
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Machine learning-based new classification for immune infiltration of gliomas. by Feng Yuan, Yingshuai Wang, Lei Yuan, Lei Ye, Yangchun Hu, Hongwei Cheng, Yan Li

“…Then establish and verify the classification model through Machine Learning (ML). Then, use DAVID to perform functional enrichment analysis for different immune subtypes. …”
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On classification of (self-dual) higher-spin gravities in flat space by Mattia Serrani

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