KPCA and ELM ensemble modeling of wastewater effluent quality indices

Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM

“…• Select disjoint train and test datasets The V-ELM has already been tested in a number of applications, such as hyperspectral image classification (Ayerdi, Marqués, & Graña, 2015), remote sensing data classification (Han & Liu, 2015) and natural gas reservoir characterization (Anifowose, Labadin, & Abdulraheem, in press), wastewater quality index modeling (Zhao, Yuan, Chai, & Tang, 2011), and intrusion detection (Fossaceca, Mazzuchi, & Sarkani, 2015) with the enhancement of multikernel learning. This basic architecture has been modified in the literature, for instance soft-class dependent voting schemes (Cao et al, 2015) provide improved reliability and sparseness of the model, a distributed approach allows to perform classification in P2P networks (Sun, Yuan, & Wang, 2011), and delta test strategy for hidden units selection enhances the construction of ensembles in Yu et al (2014).…”

Predicting Protein–Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence

“…Chen et al [ 35 ] reported an approach that uses XGBoost to reduce feature noise and adopts StackPPI that several ensemble classifiers to detect the interaction of protein pairs. Zhao et al [ 36 ] proposed an ensemble method and the results of the proposed method obtained good performance. Yousef et al [ 37 ] developed a sequence-based, fast, and adaptive PPIs prediction method, which employed principal component analysis (PCA) as a proper feature extraction method and utilized adaptive learning vector quantization (LVQ) to predict different PPI datasets.…”