• iRSpot-EL

  • Referenced in 29 articles [sw24776]
  • identify recombination spots with an ensemble learning approach. MOTIVATION: Coexisting in a DNA system, meiosis ... dinucleotide-based auto-cross covariance into an ensemble classifier of clustering approach. Five-fold cross...
  • iDHS-EL

  • Referenced in 10 articles [sw22426]
  • pseudo nucleotide composition into an ensemble learning framework. MOTIVATION: Regulatory DNA elements are associated with ... this study, using the strategy of ensemble learning framework, we proposed a new predictor called ... individual Random Forest (RF) classifiers into an ensemble predictor. The three RF operators were respectively...
  • iEnhancer-EL

  • Referenced in 11 articles [sw27668]
  • identifying enhancers and their strength with ensemble learning approach. Motivation: Identification of enhancers and their...
  • Imbalanced-learn

  • Referenced in 9 articles [sw21535]
  • imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state ... over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox depends only...
  • Learn++

  • Referenced in 8 articles [sw37991]
  • does not forget previously acquired knowledge. Learn++ utilizes ensemble of classifiers by generating multiple hypotheses ... error of the classifiers constructed by Learn++ is also provided...
  • EnsembleSVM

  • Referenced in 3 articles [sw10892]
  • EnsembleSVM: a library for ensemble learning using support vector machines. EnsembleSVM is a free software ... package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models...
  • ENDER

  • Referenced in 15 articles [sw12831]
  • thoroughly analyze a learning algorithm, called ENDER, which constructs an ensemble of decision rules. This ... problems. It uses the boosting approach for learning, which can be treated as generalization ... rules already present in the ensemble. We consider different loss functions and minimization techniques often...
  • RandGA

  • Referenced in 4 articles [sw35808]
  • algorithm for variable selection. Recently, the ensemble learning approaches have been proven to be quite ... models. In general, a good variable selection ensemble should consist of a diverse collection...
  • PLANET

  • Referenced in 5 articles [sw15434]
  • Planet: Massively parallel learning of tree ensembles with mapreduce. Classification and regression tree learning ... models over large datasets. PLANET defines tree learning as a series of distributed computations ... classification and regression trees, as well as ensembles of such models. We discuss the benefits ... using a MapReduce compute cluster for tree learning, and demonstrate the scalability of this approach...
  • DrugE-Rank

  • Referenced in 2 articles [sw23937]
  • candidate drugs or targets by ensemble learning to rank. Motivation: Identifying drug–target interactions ... types: feature-based and similarity-based methods. Learning to rank is the most powerful technique ... used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined...
  • PBoostGA

  • Referenced in 3 articles [sw17431]
  • ranked suitably. In recent years, ensemble learning has gained a significant interest in the context ... widespread success of boosting algorithms, a novel ensemble method PBoostGA is developed in this paper...
  • mlr3pipelines

  • Referenced in 3 articles [sw31527]
  • data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as ’mlr3’ ’Learners...
  • BART-BMA

  • Referenced in 5 articles [sw23498]
  • considered a Bayesian version of machine learning tree ensemble methods where the individual trees ... dimensional data is random forests, a machine learning algorithm which grows trees using a greedy...
  • Learn++.MF

  • Referenced in 5 articles [sw37993]
  • missing feature problem. We introduce Learn++.MF, an ensemble-of-classifiers based algorithm that employs ... supervised classification. Unlike most established approaches, Learn++.MF does not replace missing values with estimated ... underlying data distribution. Instead, it trains an ensemble of classifiers, each on a random subset ... include the missing features. We show that Learn++.MF can accommodate substantial amount of missing...
  • pycobra

  • Referenced in 1 article [sw21382]
  • Pycobra: A Python Toolbox for Ensemble Learning and Visualisation. We introduce pycobra, a Python library ... devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation ... several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing...
  • h2oEnsemble

  • Referenced in 1 article [sw33570]
  • package h2oEnsemble: H2O Ensemble Learning. The h2oEnsemble R package provides functionality to create ensembles from ... base learning algorithms that are accessible via the h2o R package (H2O version ... above). This type of ensemble learning is called ”super learning”, ”stacked regression” or ”stacking ... super learner ensemble represents an asymptotically optimal system for learning...
  • ArrayMining

  • Referenced in 2 articles [sw25183]
  • data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods ... analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross...
  • LibD3C

  • Referenced in 4 articles [sw21627]
  • dynamic selection strategy. Selective ensemble is a learning paradigm that follows an “overproduce and choose ... this approach is a hybrid model of ensemble pruning that is based on k-means...
  • Comet

  • Referenced in 2 articles [sw22899]
  • COMET: A Recipe for Learning and Using Large Ensembles on Massive Data. COMET ... algorithm for learning on large-scale data. It builds multiple random forest ensembles on distributed ... them into a mega-ensemble. This approach is appropriate when learning from massive-scale data ... both accuracy and training time) to learning on a sub sample of data using...
  • NeC4.5

  • Referenced in 12 articles [sw20994]
  • examples are also generated from the trained ensemble and added to the new training ... from the new training set. Since its learning results are decision trees, the comprehensibility ... better than that of neural network ensemble. Moreover, experiments show that the generalization ability...