Assessing the lifetime performance index of Rayleigh products based on the Bayesian estimation under progressive type II right censored samples. - Wen-Chuan Lee; Jong-Wuu Wu; Mei-Ling Hong; Liang-Sian Lin; Ruei-Ling Chan (J. Comput. Appl. Math., 2011)

A new approach to assess product lifetime performance for small data sets. - Der-Chiang Li; Liang-Sian Lin (Eur. J. Oper. Res., 2013)

Generating information for small data sets with a multi-modal distribution. - Der-Chiang Li; Liang-Sian Lin (Decis. Support Syst., 2014)

Improving learning accuracy by using synthetic samples for small datasets with non-linear attribute dependency. - Der-Chiang Li; Liang-Sian Lin; Li-Jhong Peng (Decis. Support Syst., 2014)

Non-parametric Statistical Assistance in Virtual Sample Selection for Small Data Set Prediction. - Yao-San Lin; Liang-Sian Lin; Der-Chiang Li; Wei-Lin Liao (ACIT-CSI, 2015)

Generating Multi-modality Virtual Samples with Soft DBSCAN for Small Data Set Learning. - Liang-Sian Lin; Der-Chiang Li; Wei-Hao Yu; Yu-Mei Hsueh (ACIT-CSI, 2015)

Extending Sample Information for Small Data Set Prediction. - Hung-Yu Chen; Der-Chiang Li; Liang-Sian Lin (IIAI-AAI, 2016)

Improving Virtual Sample Generation for Small Sample Learning with Dependent Attributes. - Liang-Sian Lin; Der-Chiang Li; Chih-Wei Pan (IIAI-AAI, 2016)

A Learning Approach with Under-and Over-Sampling for Imbalanced Data Sets. - Chun-Wu Yeh; Der-Chiang Li; Liang-Sian Lin; Tung-I Tsai (IIAI-AAI, 2016)

The attribute-trend-similarity method to improve learning performance for small datasets. - Der-Chiang Li; Wu-Kuo Lin; Liang-Sian Lin; Chien-Chih Chen; Wen-Ting Huang (Int. J. Prod. Res., 2017)

Rebuilding sample distributions for small dataset learning. - Der-Chiang Li; Wu-Kuo Lin; Chien-Chih Chen; Hung-Yu Chen; Liang-Sian Lin (Decis. Support Syst., 2018)

An attribute extending method to improve learning performance for small datasets. - Liang-Sian Lin; Der-Chiang Li; Hung-Yu Chen; Yu-Chun Chiang (Neurocomputing, 2018)

Improving the Learning Accuracy of the Mega Trend Diffusion Technique for Small Data Sets. - Tung-I Tsai; Chun-Wu Yeh; Liang-Sian Lin; I-Hsiang Wen (IIAI-AAI, 2019)

Using virtual samples to improve learning performance for small datasets with multimodal distributions. - Der-Chiang Li; Liang-Sian Lin; Chien-Chih Chen; Wei-Hao Yu (Soft Comput., 2019)

Improving Anomaly Detection in IoT-Based Solar Energy System Using SMOTE-PSO and SVM Model. - Liang-Sian Lin; Zhi-Yu Chen; Yu Wang; Li-Wei Jiang (MLIS, 2022)

IoT-Based Driver Distraction Prevention System. - Liang-Sian Lin; Li-Wei Jiang; Yu Wang; Zhi-Yu Chen; Yi-Jie Li (MLIS, 2022)

A Boundary-Information-Based Oversampling Approach to Improve Learning Performance for Imbalanced Datasets. - Der-Chiang Li; Qi-Shi Shi; Yao-San Lin; Liang-Sian Lin (Entropy, 2022)

An Integrated Framework Based on GAN and RBI for Learning with Insufficient Datasets. - Yao-San Lin; Liang-Sian Lin; Chih-Ching Chen (Symmetry, 2022)

Generating virtual samples to improve learning performance in small datasets with non-linear and asymmetric distributions. - Liang-Sian Lin; Yao-San Lin; Der-Chiang Li (Neurocomputing, 2023)

Mega trend diffusion-siamese network oversampling for imbalanced datasets' SVM classification. - Liang-Sian Lin; Yao-San Lin; Der-Chiang Li; Yi-Ting Chen (Appl. Soft Comput., 2023)

Improved learning performance for small datasets in high dimensions by new dual-net model for non-linear interpolation virtual sample generation. - Liang-Sian Lin; Yao-San Lin; Der-Chiang Li; Yun-Hsuan Liu (Decis. Support Syst., 2023)

Improving small sample prediction performance via novel nonlinear interpolation virtual sample generation with self-supervised learning. - Liang-Sian Lin (Inf. Sci., 2024)

Enhancing prediction accuracy for high-dimensional small-sample-size microarray data cancer by combining chebyshev interpolation with new dual-net GAN. - Liang-Sian Lin; Yao-San Lin; Der-Chiang Li; Susan C. Hu; Chih-I Huang (Appl. Soft Comput., 2025)