Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge

Li, Der-Chiang; Wu, Chien‐Te; Tsai, Tsung‐Lin; Lina, Yao-San

doi:10.1016/j.cor.2005.05.019

Cited by 199 publications

(81 citation statements)

References 14 publications

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“…7. Instead of assuming the distributions of majority and minority of the dataset, this learning method introduces fuzzy set theory to build up the possible fuzzy membership functions: for the larger dataset, based on the central limit theory in statistics Gaussian function is applied to reduce the size of the dataset using α-cut; for the smaller dataset, mega-trend diffusion (MTD) function proposed by Li et al (Li et al, 2007) is introduced to amplify the size of the dataset. After balancing the datasets, a step called attribute extension was used to collect the omitted information during the reduction of the majority dataset and to create new attributes computed by the corresponding fuzzy membership functions of majority and minority dataset.…”

Section: Datasets Balancing By Fuzzy Set Methodsmentioning

confidence: 99%

Improved feature-based prediction of SNPs in human cytochrome P450 enzymes

Xiong

Zhang

et al. 2015

Interdiscip Sci Comput Life Sci

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Single nucleotide polymorphisms (SNPs) make up the most common form of mutations in human cytochrome P450 enzymes family, and have the potential to bring with different drug responses or specific diseases in individual patients. Here, based on machine learning technology, we aim to explore an effective set of sequence-based features for improving prediction of SNPs by using support vector machine algorithms. The features are derived from the target residues and flanking protein sequences, such as amino acid types, sequences composition, physicochemical properties, position-specific scoring matrix, phylogenetic entropy and the number of possible codons of target residues. In order to deal with the imbalance data with a majority of non-SNPs and a minority of SNPs, a preprocessing strategy based on fuzzy set theory was applied to the datasets. Our final model achieves the performance of 93.8% in sensitivity, 88.8% in specificity, 91.3% in accuracy and 0.971 of AUC value, which is significantly higher than the previous DNA sequence-based or protein sequence-based methods. Furthermore, our study also suggested the roles of individual features for prediction of SNPs. The most important features consist of the amino acid type, the number of available codons, position-specific scoring matrix and phylogenetic entropy. The improved model will be a promising tool for SNP predictions, and assist in the research of genome mutation and personalized prescriptions.

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Section: Datasets Balancing By Fuzzy Set Methodsmentioning

confidence: 99%

Improved feature-based prediction of SNPs in human cytochrome P450 enzymes

Xiong

Zhang

et al. 2015

Interdiscip Sci Comput Life Sci

View full text Add to dashboard Cite

show abstract

“…As explained previously, the available experimental data points (43 data points) do not provide sufficient information for training robust prediction neural network. Therefore, the research of Li et al [58] was referred to, and the megatrend-diffusion and estimation of domain range techniques were used to create virtual data (adding a number of virtual data points) to construct the predicted model. Because the purpose of this research is to apply the procedure proposed by Li et al to construct a prediction model, the details of the derivation of the methodology are omitted here.…”

Section: Detailed Steps In the Ann Modeling Proceduresmentioning

confidence: 99%

Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks

Papari

Yousefi

Moghadasi

et al. 2011

International Journal of Thermal Sciences

116

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“…It includes attributes of infinite-length, concept-evolution and data drift. The procedure to aid the valuation of domain samples methodically is proposed as Mega-Trend-Diffusion Technique (MTDF) in [33] to address the class imbalance problem. A recent imbalanced data set handling technique i.e.…”

Section: Related Workmentioning

confidence: 99%