Infinite Latent Feature Selection Technique for Hyperspectral Image Classification

Miftahushudur, Tajul; Wael, Chaeriah Bin Ali; Praludi, Teguh

doi:10.14203/jet.v19.32-37

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…The ILFS technique consists of 3 main processes. The first is the preprocessing step, followed by the weighting graph and sorting [11]. During the first stage, each feature is represented with the help of an identifier that reveals its distinctiveness.…”

Section: Infinite Latent Feature Selection (Ilfs)mentioning

confidence: 99%

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Tuncer

2022

Applied Computer Systems

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The aim of the study is to diagnose Covid-19 by machine learning algorithms using biochemical parameters. In addition to the aim of the study, October selection was performed using 14 different feature selection methods based on the biochemical parameters available to us. As a result of the study, the performance of the algorithms and feature selection methods was evaluated using performance evaluation criteria. The dataset used in the study consists of 100 covid-negative and 121 covid-positive data from a total of 221 patients. The dataset includes 16 biochemical parameters used for the diagnosis of Covid-19. Feature selection methods were used to reduce the number of parameters and perform the classification process. The result of the study shows that the new feature set obtained using feature selection algorithms yields very similar results to the set containing all features. Overall, 5 features obtained from 16 features by feature selection methods yielded the best performance for the K-Nearest Neighbour algorithm with the FSVFS feature selection method of 86.4 %.

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Section: Infinite Latent Feature Selection (Ilfs)mentioning

confidence: 99%

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Tuncer

2022

Applied Computer Systems

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“…ILFS (Infinite Latent Feature Selection) là một kỹ thuật bao gồm ba bước như tiền xử lý, trọng số đặc trưng dựa trên biểu đồ được kết nối đầy đủ trong mỗi nút kết nối tất cả các đặc trưng. Cuối cùng, điểm số của độ dài đường dẫn được tính toán, sau đó xếp hạng tương ứng với đặc trưng (Miftahushudur, Wael, & Praludi, 2019).…”

Section: Ensemble Feature Selectionunclassified

Phương pháp tiếp cận quần thể lựa chọn đặc trưng dựa trên xếp hạng đặc trưng cho phân loại hình ảnh hạt lúa giống

Dzi¹

2022

HCMCOUJS - KỸ THUẬT VÀ CÔNG NGHỆ

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Trong nông nghiệp thông minh, người ta sử dụng thị giác máy tính để nhận dạng hạt lúa giống thay vì cần các chuyên gia thực hiện. Trong bài báo này, chúng tôi đã xem xét ba loại mô tả đặc trưng, như Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) và Gradient Domain Image Stitching (GIST) để mô tả hình ảnh hạt lúa giống. Tuy nhiên, cách tiếp cận này làm nảy sinh vấn đề về hiện tượng số chiều và cần phải lựa chọn các đặc trưng liên quan để có một mô hình biểu diễn nhỏ gọn và tốt hơn. Một quần thể lựa chọn đặc trưng mới được đề xuất để đại diện cho tất cả các thông tin hữu ích được thu thập từ các phương pháp lựa chọn đặc trưng đơn lẻ khác nhau. Các kết quả thử nghiệm trên phương pháp đề xuất của chúng tôi đã cho thấy được hiệu quả về độ chính xác.

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Application of novel hybrid machine learning systems and radiomics features for non-motor outcome prediction in Parkinson’s disease

Salmanpour¹,

Bakhtiary²,

Hosseinzadeh³

et al. 2023

Phys. Med. Biol.

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Objectives: Parkinson's disease (PD) is a complex neurodegenerative disorder, affecting 2–3% of the elderly population. Montreal Cognitive Assessment (MoCA), a rapid nonmotor screening test, assesses different cognitive dysfunctionality aspects. Early MoCA prediction may facilitate better temporal therapy and disease control. Radiomics features (RF), in addition to clinical features (CF), are indicated to increase clinical diagnoses, etc., bridging between medical imaging procedures and personalized medicine. We investigate the effect of RFs, CFs, and conventional imaging features (CIF) to enhance prediction performance using hybrid machine learning systems (HMLS). Methods: We selected 210 patients with 981 features (CFs, CIFs, and RFs) from the Parkinson's Progression-Markers-Initiative database. We generated 4 datasets, namely using (i,ii) year-0 (D1) or year-1 (D2) features, iii) longitudinal data (D3, putting datasets in years 0&1 longitudinally next to each other), and iv) timeless data (D4, effectively doubling dataset size by listing both datasets from years 0&1 separately). First, we directly applied 23 predictor algorithms (PA) to the datasets to predict year-4 MoCA, which PD patients this year have a higher dementia risk. Subsequently, HMLSs, including 14 attribute extraction (AEA) and 10 feature selection algorithms (FSA) followed by PAs were employed to enhance prediction performances. 80% of all datapoints were utilized to select the best model based on minimum mean absolute error (MAE) resulting from 5-fold cross-validation. Subsequently, the remaining 20% was used for hold-out testing of the selected models. Results: When applying PAs without ASAs/FEAs to datasets (MoCA outcome range: [11,30]), Adaboost achieved a MAE of 1.74±0.29 on D4 with a hold-out testing performance of 1.71. When employing HMLSs, D4+Minimum_Redundancy_Maximum_Relevance (MRMR)+K_Nearest_Neighbor Regressor achieved the highest performance of 1.05±0.25 with a hold-out testing performance of 0.57. Conclusion: Our study shows the importance of using larger datasets (timeless), and utilizing optimized HMLSs, for significantly improved prediction of MoCA in PD patients.

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Infinite Latent Feature Selection Technique for Hyperspectral Image Classification

Cited by 5 publications

References 17 publications

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Phương pháp tiếp cận quần thể lựa chọn đặc trưng dựa trên xếp hạng đặc trưng cho phân loại hình ảnh hạt lúa giống

Application of novel hybrid machine learning systems and radiomics features for non-motor outcome prediction in Parkinson’s disease

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