Evolving fuzzy systems are data-driven fuzzy (rule-based) systems supporting an incremental model adaptation in dynamically changing environments; typically, such models are learned on a continuous stream of data in an online manner. This paper advocates the use of visualization techniques in order to help a user gain insight into the process of model evolution. More specifically, rule chains a...

We ask the question whether the quark and gluon distributions in the Pomeron obtained from QCD fits to hard diffraction processes at HERA can be dynamically generated from a state made of valence-like gluons and sea quarks as input. By a method combining backward Q-evolution for data exploration and forward Q-evolution for a best fit determination, we find that the diffractive structure functio...

This paper presents an experimental study for turning process in machining by using Takagi–Sugeno– Kang (TSK) fuzzy modeling to accomplish the integration of multi-sensor information and tool wear information. It generates fuzzy rules directly from the input–output data acquired from sensors, and provides high accuracy and high reliability of the tool wear prediction over a wide range of cuttin...

This article considers receiver operating characteristic (ROC) analysis for bivariate marker measurements. The research interest is to extend tools and rules from univariate marker to bivariate marker setting for evaluating predictive accuracy of markers using a tree-based classification rule. Using an and-or classifier, an ROC function together with a weighted ROC function (WROC) and their con...

This paper gives some known theoretical results about fuzzy rule-based classifiers and offers a few new ones. The ability of Takagi-Sugeno-Kang (TSK) fuzzy classifiers to match exactly and to approximate classification boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a classification boundary in R (2) is extended from monotonous to arbitrary functions. Equivale...

In this study a simulation-optimization model is developed for deriving operation rule-curves in drought ‎periods. To each reservoir, two rule-curves with adjustable monthly levels are introduced dividing the ‎reservoir capacity into three zones between the normal water level and minimum operation level. To each ‎zone of a reservoir and for each month of the year a hedging coefficient is introd...

This paper investigates how to adapt standard classification rule learning approaches to subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorit...

Objective: the present meta-analysis was designed to determine the value of Pediatric Emergency Care Applied Research Network (PECARN) rule in prediction of clinically important traumatic brain injury (ciTBI).Methods: Extensive search was conducted in the databases of Medline, Embase, Scopus, Web of Sciences, Cinahl up to the end of August 2017. The search records were screened and summarized b...

In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...