• iPPBS-Opt

  • Referenced in 16 articles [sw22428]
  • training dataset; (2) the ensemble voting approach to select the most relevant features...
  • RandGA

  • Referenced in 4 articles [sw35808]
  • parallel genetic algorithm for variable selection. Recently, the ensemble learning approaches have been proven ... models. In general, a good variable selection ensemble should consist of a diverse collection ... increase the diversity among ensemble members. Using a number of simulated data sets, we show ... method RandGA compares favorably with other variable selection techniques. As a real example...
  • DESlib

  • Referenced in 3 articles [sw23559]
  • DESlib: A Dynamic ensemble selection library in Python. DESlib is an open-source python library ... containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation...
  • LibD3C

  • Referenced in 4 articles [sw21627]
  • clustering and dynamic selection strategy. Selective ensemble is a learning paradigm that follows an “overproduce ... classifiers that are accurate and diverse are selected to solve a problem. In this paper ... ensemble pruning that is based on k-means clustering and the framework of dynamic selection...
  • ArrayMining

  • Referenced in 2 articles [sw25183]
  • algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization ... access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross ... combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration ... algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation...
  • boost

  • Referenced in 41 articles [sw35655]
  • highly correlated input variables, perform feature selection and provide class probability estimates that serve ... promising solution is to combine the two ensemble schemes bagging and boosting to a novel...
  • BartPy

  • Referenced in 83 articles [sw40584]
  • dimensionally adaptive random basis elements. Motivated by ensemble methods in general, and boosting algorithms ... also be used for model-free variable selection. BART’s many features are illustrated with...
  • ccprmod

  • Referenced in 2 articles [sw08562]
  • model of classifier competence for dynamic ensemble selection, Pattern Recognition, Volume 44, Issues...
  • CSMES

  • Referenced in 1 article [sw33191]
  • package CSMES: Cost-Sensitive Multi-Criteria Ensemble Selection for Uncertain Cost Conditions. Functions for cost ... sensitive multi-criteria ensemble selection (CSMES) (as described in De bock...
  • BayesTree

  • Referenced in 64 articles [sw07995]
  • dimensionally adaptive random basis elements. Motivated by ensemble methods in general, and boosting algorithms ... also be used for model-free variable selection. BART’s many features are illustrated with...
  • inTrees

  • Referenced in 6 articles [sw41498]
  • Interpreting Tree Ensembles with inTrees. Tree ensembles such as random forests and boosted trees ... extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions...
  • mRMRe

  • Referenced in 2 articles [sw28072]
  • Parallelized Minimum Redundancy, Maximum Relevance (mRMR) Ensemble Feature Selection”. ”Computes mutual information matrices from continuous ... feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique with...
  • PBoostGA

  • Referenced in 3 articles [sw17431]
  • selection, is actually more fundamental since selection can be realized by thresholding once the variables ... years, ensemble learning has gained a significant interest in the context of variable selection ... great potential to improve selection accuracy and to reduce the risk of falsely including some ... ensemble method PBoostGA is developed in this paper to implement variable ranking and selection...
  • ESKNN

  • Referenced in 1 article [sw26841]
  • optimal models are selected in the ensemble from an initially large set of base ... assessment is applied in selection of optimal models for the ensemble in the training function...
  • RPEnsemble

  • Referenced in 2 articles [sw16698]
  • classification. http://arxiv.org/abs/1504.04595”. The random projection ensemble classifier is a general method for classification ... test error is selected. The random projection ensemble classifier then aggregates the results of applying...
  • LSCP

  • Referenced in 1 article [sw41893]
  • LSCP: Locally Selective Combination in Parallel Outlier Ensembles. In unsupervised outlier ensembles, the absence ... Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting ... framework---called Locally Selective Combination in Parallel Outlier Ensembles (LSCP)---which addresses the issue ... consensus of its nearest neighbors in randomly selected feature subspaces. The top-performing base detectors...
  • Learn++.MF

  • Referenced in 5 articles [sw37993]
  • ensemble-of-classifiers based algorithm that employs random subspace selection to address the missing feature ... underlying data distribution. Instead, it trains an ensemble of classifiers, each on a random subset...
  • ASTALAVISTA

  • Referenced in 3 articles [sw17177]
  • reference annotations (GENCODE, REFSEQ, ENSEMBL) as well as for genes selected by the user according...
  • Signal-3L

  • Referenced in 16 articles [sw26857]
  • secretory or non-secretory by an ensemble classifier formed by fusing many individual ... PseAA (pseudo amino acid) composition spaces; (2) selecting a set of candidates for the possible...
  • gbts

  • Referenced in 0 articles [sw19110]
  • Trees constructed via the method of ensemble selection...