Customer value segmentation based on cost-sensitive learning Support Vector Machine

2010 ◽  
Vol 14 (1) ◽  
pp. 126 ◽  
Author(s):  
Zou Peng ◽  
Hao Yuanyuan ◽  
Li Yijun
Keyword(s):  
Support Vector Machine ◽  
Customer Value ◽  
Support Vector ◽  
Learning Support ◽  
Cost Sensitive Learning

Medical Image Retrieval Based on Semi-Supervised Learning

2010 ◽  
Vol 108-111 ◽  
pp. 201-206 ◽  
Author(s):  
Hui Liu ◽  
Cai Ming Zhang ◽  
Hua Han
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Image Retrieval ◽  
Supervised Learning ◽  
Relevance Feedback ◽  
Medical Image ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Learning Support ◽  
Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

scholarly journals ANALISIS KESEHATAN TERUMBU KARANG BERDASARKAN KARAKTERISTIK SUNGAI, LAUT, DAN POPULASI AREA PEMUKIMAN MENGGUNAKAN MACHINE LEARNING

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Adinda miftahul Ilmi Habiba ◽  
Agi Prasetiadi ◽  
Cepi Ramdani
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Learning Support ◽  
K Nearest Neighbor

Penelitian ini untuk mengetahui kualitas kesehatan terumbu karang disuatu wilayah di Indonesia dengan mengambil beberapa faktor seperti wisatawan yang datang, latitude, longtitude, suhu, tahun, populasi warga, jumlah pemuda, dan jumlah industri, dan metode yang digunakan adalah machine learning dengan algoritma K-Nearest Neighbor, Support Vector Machine, dan Ensemble Classifier, untuk ensemble menggunkan randomforest untuk mengambil cabang-cabang pohon atau fitur keputusan yang paling relevan dengan output, penelitian ini diharapkan bisa menjadi acuan bagi wilayah yang kondisi terumbu karangnya masih kurang baik dapat mencontoh wilayah yang kondisi terumbu karangnya sudah baik dengan melihat faktor apa saja yang mempengaruhi terumbu karang disuatu wilayah itu masuk kategori baik. Hasil akhir dari penelitian ini pada algoritma K-Nearest Neighbor faktor yang berpengaruh bagi kesehatan terumbu karang yaitu wisatawan yang datang, latitude, longtitude, suhu, tahum dan pupulasi warga, sementara pada algoritma Support Vector Machine faktor yang berpengaruh wisatawan yang datang, Latitude, suhu dan tahun untuk algoritma Ensemble Classifier faktor yang berpengaruh wisatawan yang datang, latitude, longtitude, suhu dan jumlah industry, Pada kasus ini algoritma Support Vector Machine memiliki kinerja lebih baik dibandingkan K-Nearest Neighbor dan Ensemble Classifier.Kata Kunci: Ekosistem, Ensemble Classifier, K-Nearest Neighbor, Machine Learning, Support Vector Machine 

Hyperspectral Imaging with Cost-Sensitive Learning for High-Throughput Screening of Loblolly Pine (Pinus taeda L.) Seedlings for Freeze Tolerance

2021 ◽  
Vol 64 (6) ◽  
pp. 2045-2059
Author(s):  
Yuzhen Lu ◽  
Kitt G. Payn ◽  
Piyush Pandey ◽  
Juan J. Acosta ◽  
Austin J. Heine ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Imaging ◽  
Loblolly Pine ◽  
High Throughput Screening ◽  
Freeze Tolerance ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Full Spectrum ◽  
Cost Sensitive Learning ◽  
Pinus Taeda L

HighlightsA hyperspectral imaging approach was developed for freeze-tolerance phenotyping of loblolly pine seedlings.Image acquisition was conducted before and periodically after artificial freezing of the seedlings.A hyperspectral data processing pipeline was developed to extract the spectra from seedling segments.Cost-sensitive support vector machine (SVM) was used for classifying stressed and healthy seedlings.Post-freeze scanning of seedlings on day 41 achieved the highest screening accuracy of 97%.Abstract. Loblolly pine (Pinus taeda L.) is a commercially important timber species planted across a wide temperature gradient in the southeastern U.S. It is critical to ensure that the planting stock is suitably adapted to the growing environment to achieve high productivity and survival. Long-term field studies, although considered the most reliable method for assessing cold hardiness of loblolly pine, are extremely resource-intensive and time-consuming. The development of a high-throughput screening tool to characterize and classify freeze tolerance among different genetic entries of seedlings will facilitate accurate deployment of highly productive and well-adapted families across the landscape. This study presents a novel approach using hyperspectral imaging to screen loblolly pine seedlings for freeze tolerance. A diverse population of 1549 seedlings raised in a nursery were subjected to an artificial mid-winter freeze using a freeze chamber. A custom-assembled hyperspectral imaging system was used for in-situ scanning of the seedlings before and periodically after the freeze event, followed by visual scoring of the frozen seedlings. A hyperspectral data processing pipeline was developed to segment individual seedlings and extract the spectral data. Examination of the spectral features of the seedlings revealed reductions in chlorophylls and water concentrations in the freeze-susceptible plants. Because the majority of seedlings were freeze-stressed, leading to severe class imbalance in the hyperspectral data, a cost-sensitive learning technique that aims to optimize a class-specific cost matrix in classification schemes was proposed for modeling the imbalanced hyperspectral data, classifying the seedlings into healthy and freeze-stressed phenotypes. Cost optimization was effective for boosting the classification accuracy compared to regular modeling that assigns equal costs to individual classes. Full-spectrum, cost-optimized support vector machine (SVM) models achieved geometric classification accuracies of 75% to 78% before and within 10 days after the freeze event, and up to 96% for seedlings 41 days after the freeze event. The top portions of seedlings were more indicative of freeze events than the middle and bottom portions, leading to better classification accuracies. Further, variable selection enabled significant reductions in wavelengths while achieving even better accuracies of up to 97% than full-spectrum SVM modeling. This study demonstrates that hyperspectral imaging can provide tree breeders with a valuable tool for improved efficiency and objectivity in the characterization and screening of freeze tolerance for loblolly pine. Keywords: Cost-sensitive learning, Freeze tolerance, Hyperspectral imaging, Plant phenotyping, Support vector machine.

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