• DENFIS

  • Referenced in 58 articles [sw24183]
  • prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including ... through local element tuning. New fuzzy rules are created and updated during the operation ... DENFIS before or during its learning process. Fuzzy rules can also be extracted during...
  • AdaBoost.MH

  • Referenced in 456 articles [sw08517]
  • decision-theoretic generalization of on-line learning and an application to boosting. In the first ... that the multiplicative weight-update Littlestone-Warmuth rule can be adapted to this model, yielding ... considerably more general class of learning problems. We show how the resulting learning algorithm...
  • LERS

  • Referenced in 121 articles [sw08637]
  • learning from examples based on rough sets. The paper presents the system LERS for rule ... possible rules. The user has the choice to use the machine learning approach ... second case, the system induces all rules, each in the minimal form, that...
  • ANFIS

  • Referenced in 266 articles [sw08730]
  • adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input ... form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation...
  • ENDER

  • Referenced in 14 articles [sw12831]
  • important role in machine learning. The main advantage of decision rules is their simplicity ... analyze a learning algorithm, called ENDER, which constructs an ensemble of decision rules. This algorithm ... learning, which can be treated as generalization of sequential covering. Each new rule is fitted...
  • PRIE

  • Referenced in 5 articles [sw28405]
  • learn. Existing work in classification rule learning assumes the goal is to produce categorical classifications ... This paper presents a method for learning rules directly from ROC space when the goal ... curve (AUC). Basic principles from rule learning and computational geometry are used to focus ... promising rule combinations. The result is a system that can learn intelligible rulelists with good...
  • GP-COACH

  • Referenced in 10 articles [sw09137]
  • COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems ... based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems ... High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means ... constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH...
  • ETPS

  • Referenced in 153 articles [sw06302]
  • number of years. Students generally learn to use ETPS fairly quickly just by reading ... student using ETPS issues commands to apply rules of inference in specified ways...
  • SparseFIS

  • Referenced in 10 articles [sw13736]
  • deal with a novel data-driven learning method [sparse fuzzy inference systems (SparseFIS)] for Takagi ... fuzzy systems, extended by including rule weights. Our learning method consists of three phases ... widths) in the antecedent parts of the rules. Hereby, the number of clusters = rules ... constrained-optimization procedure for each rule separately (local learning approach). Sparsity constraints are applied...
  • RIONA

  • Referenced in 9 articles [sw30227]
  • classification system combining rule induction and instance-based learning. The article describes a method combining ... widely-used empirical approaches to learning from examples: rule induction and instance-based learning ... whole support set of all rules matching a test case, but the support set restricted ... whole learning set. The combination of (k)-NN and a rule-based algorithm results...
  • FURIA

  • Referenced in 12 articles [sw19135]
  • advantages, such as simple and comprehensible rule sets. In addition, it includes a number ... modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered...
  • DENDRAL

  • Referenced in 8 articles [sw23919]
  • total hypothesis space according to heuristic rules learned from chemists...
  • Fuzzy Logic Toolbox

  • Referenced in 56 articles [sw07379]
  • neurofuzzy learning. The toolbox lets you model complex system behaviors using simple logic rules...
  • CMAR

  • Referenced in 48 articles [sw28406]
  • association rules. Our extensive experiments on 26 databases from the UCI machine learning database repository...
  • HyFIS

  • Referenced in 12 articles [sw24184]
  • fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic ... connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally ... examples by a hybrid learning scheme comprised of two phases: rule generation phase from data ... rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate...
  • GuideR

  • Referenced in 3 articles [sw30224]
  • GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings. This ... preferences or domain knowledge to the rule learning process. Automatic selection of attributes and attribute ... leads to the situation in which resulting rules do not contain interesting information. We propose ... RuleKit - our comprehensive suite for rule-based learning. We suggest using RuleKit for analyses...
  • RRIA

  • Referenced in 16 articles [sw02558]
  • RRIA: A rough set and rule tree based incremental knowledge acquisition algorithm. As a special ... which the human brain is learning new knowledge, incremental learning is an important topic ... knowledge quickly, based on original knowledge learned before, and in such way that the knowledge ... rule tree based incremental knowledge acquisition algorithm. It can learn from a domain data...
  • RWeka

  • Referenced in 15 articles [sw07208]
  • source project in machine learning covering classification, regression, clustering, association rules and visualization...
  • PANFIS

  • Referenced in 6 articles [sw13735]
  • overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning ... degree of flexibility capable of assembling its rule base autonomously according to the degree ... commence its learning process from scratch with an empty rule base. The fuzzy rules ... transparent rule base escalating human’s interpretability. The learning and modeling performances of the proposed...
  • FOIL-D

  • Referenced in 3 articles [sw02584]
  • success on a variety of multi-relational rule mining tasks, however, most ILP systems ... decrease the computational cost of learning a set of rules. We present experimental results that ... standard ILP datasets, the rule sets learned using our extensions are equivalent to those learned...