• SMOTE

  • Referenced in 128 articles [sw34239]
  • classifier to the minority class. This paper shows that a combination of our method ... majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling ... class. This paper also shows that a combination of our method of over-sampling ... sampling the majority class can achieve better classifier performance (in ROC space) than varying...
  • PASCAL VOC

  • Referenced in 120 articles [sw36507]
  • joint classifier over the submitted algorithms in order to measure their complementarity and combined performance...
  • SLIQ

  • Referenced in 50 articles [sw11759]
  • classifier. SLIQ is a decision tree classifier that can handle both numeric and categorical attributes ... combination of these techniques enables SLIQ to scale for large data sets and classify data...
  • BAYES-NEAREST

  • Referenced in 4 articles [sw02847]
  • BAYES-NEAREST: a new hybrid classifier combining Bayesian network and distance based algorithms. This paper ... presents a new hybrid classifier that combines the probability based Bayesian Network paradigm with ... Nearest Neighbor algorithm is used in combination with the Bayesian Network in the deduction phase ... with the well known Naive Bayes classifier in some standard databases; the results obtained...
  • Learn++

  • Referenced in 7 articles [sw37991]
  • distributions. The outputs of the resulting classifiers are combined using a weighted majority voting procedure ... upper bound on the error of the classifiers constructed by Learn++ is also provided...
  • 4eMka2

  • Referenced in 53 articles [sw16168]
  • that it bases on rough set theory combined with dominance relation, which is quite ... classification rules from a set of already classified examples. These rules could be used...
  • ROBPCA

  • Referenced in 67 articles [sw11592]
  • Here we propose the ROBPCA approach, which combines projection pursuit ideas with robust scatter matrix ... produces a diagnostic plot that displays and classifies the outliers. We apply the algorithm...
  • SPRINT

  • Referenced in 38 articles [sw11760]
  • SPRINT: a scalable parallel classifier for data mining. Classification is an important data mining problem ... here, exhibits excellent scalability as well. The combination of these characteristics makes the proposed algorithm...
  • SMILES

  • Referenced in 1 article [sw19152]
  • Simple mimetic classifiers. The combination of classifiers is a powerful tool to improve the accuracy ... classifiers, by using the prediction of multiple models and combining them. Many practical and useful ... combination techniques work by using the output of several classifiers as the input ... classifiers. More precisely, we use the combination of classifiers for labelling an invented random dataset...
  • boost

  • Referenced in 36 articles [sw35655]
  • uncertainty. A very promising solution is to combine the two ensemble schemes bagging and boosting ... module in boosting, the resulting classifier consistently improves the predictive performance and the probability estimates...
  • ROCR

  • Referenced in 42 articles [sw04551]
  • ROCR: Visualizing the performance of scoring classifiers , ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall ... cutoff-parametrized 2D performance curves by freely combining two from over 25 performance measures...
  • RIONA

  • Referenced in 9 articles [sw30227]
  • algorithm considering the whole learning set. The combination of (k)-NN and a rule-based ... using all minimal rules. Moreover, the presented classifier has high accuracy for both kinds...
  • PromoterExplorer

  • Referenced in 2 articles [sw35524]
  • features and digitized DNA sequence, and then combine them to build a high-dimensional input ... build a sequence of weak classifiers, which are combined to form a strong classifier...
  • Learn++.NC

  • Referenced in 3 articles [sw37992]
  • Learn++.NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental...
  • D-SCIDS

  • Referenced in 3 articles [sw29719]
  • This paper evaluates three fuzzy rule-based classifiers to detect intrusions in a network. Results ... SCIDS) as a combination of different classifiers to model lightweight and more accurate (heavy weight...
  • MIForests

  • Referenced in 5 articles [sw22587]
  • Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision ... computer vision. MIForest combine the advantages of these classifiers with the flexibility of multiple instance...
  • RBoost

  • Referenced in 2 articles [sw29975]
  • because of its excellent performance in combining weak classifiers into strong classifiers. However, AdaBoost tends...
  • Orbiter

  • Referenced in 5 articles [sw12417]
  • generate and classify various classes of combinatorial objects. The algorithms combine techniques from Group Theory...
  • CALI

  • Referenced in 6 articles [sw17609]
  • method combines temporal adjacency, Fuzzy Logic and geometric features to classify scribbles. This algorithm recognizes...
  • TS-CHIEF

  • Referenced in 2 articles [sw32511]
  • relevant representation. HIVE-COTE combines multiple types of classifiers: each extracting information about a specific ... novel TSC algorithm, TS-CHIEF (Time Series Combination of Heterogeneous and Integrated Embedding Forest), which ... runtime. TS-CHIEF constructs an ensemble classifier that integrates the most effective embeddings of time...