Gu, Q., Li, Z., Han, J.: Correlated multi-label feature selection. In: ACM Inter- national Conference on Information and Knowledge Management. pp. 1087-1096 (2011)Q. Gu, Z. Li, and J. Han, "Correlated multi-label feature selection," in Proceedings of the 20th ACM international ...
Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, ...关键词: Multi-label classification class imbalance label correlation multi-label feature selection DOI...
In this study, the IGR was adopted to do the selection of conditioning factors. Let us assume that the training data S consist of n input samples, and belong to the class label Yi (landslide, non-landslide). Then, the IGR for the landslide causal factor F and S could be calculated as...
Regularly, these fluorophores are used to specifically label proteins of interest within a cell either as chimeric proteins with photoactivatable fluorescent proteins (PAFPs; in PALM) or as synthetic labels of primary or secondary antibodies (STORM). The photoactivation properties of these fluorophores ...
To avoid clutter, we do not label the vertices in d–f. All graphs are weighted, i.e., the line thickness is proportional to the magnitude of the corresponding hopping coefficient. Several different clusters of configurations are visible in the case of H1. The clusters start to form ...
label-free quantification OCR oxygen consumption rate PER proton efflux rate ROS reactive oxygen species SILAC stable isotopic labeling in cell culture TLS turnover–life span slope UPS ubiquitin–proteasome system Protein homeostasis (proteostasis) encompasses an array of coordinated cellular functions that...
Adjacency graph foraH1,bH2,cH3, all forL=Np= 3.d–fsame asa,bandcbut forL=Np= 6. To avoid clutter, we do not label the vertices ind–f. All graphs are weighted, i.e., the line thickness is proportional to the magnitude of the corresponding hopping coefficient. Several d...
Then, we integrate the functional correlation into the framework of multi-label linear regression, and introduce robust sparse penalty to achieve the function assignment and representative feature selection simultaneously. For the optimization, we design an efficient algorithm to iteratively solve several ...
Probabilistic approach for multiclass classification with neural network. Artif. Neural Netw. 1991, 10, 1003–1006. 22. Ciarelli, P.M.; Oliveira, E.; Badue, C.; Souza, A.F.D. Multi-Label Text Categorization Using a Probabilistic Neural Network. Int. J. Comput. Inf. Syst. Ind. Manag...
The label “SF” indicates the selectivity filter. The network was created using software package Gephi [99]. The significant mutual influence between the residues behind the SF and the ion occupancy in specific sites means that perturbations imposed on either residues or ions affect the KcsA ...