Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification
نویسندگان
چکیده
The use of evolutionary algorithms for designing fuzzy systems provides them with learning and adaptation capabilities, resulting on what is known as Evolutionary Fuzzy Systems. These types of systems have been successfully applied in several areas of Data Mining, including standard classification, regression problems and frequent pattern mining. This is due to their ability to adapt their working procedure independently of the context we are addressing. Specifically, Evolutionary Fuzzy Systems have been lately applied to a new classification problem showing good and accurate results. We are referring to the problem of classification with imbalanced datasets, which is basically defined by an uneven distribution between the instances of the classes. In this work, we will first introduce some basic concepts on linguistic fuzzy rule based systems. Then, we will present a complete taxonomy for Evolutionary Fuzzy Systems. Then, we will review several significant proposals made in this research area that have been developed for addressing classification with imbalanced datasets. Finally, we will show a case study fromwhich wewill highlight the good behavior of Evolutionary Fuzzy Systems in this particular context.
منابع مشابه
Proposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
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 ...
متن کاملارائهروش جدید مبتنیبر برنامهنویسی ژنتیک برای وزندهی قوانین فازی در طبقهبندی نامتوازن
In classification problems, we often encounter datasets with different percentage of patterns (i.e. classes with a high pattern percentage and classes with a low pattern percentage). These problems are called “classification Problems with imbalanced data-sets”. Fuzzy rule based classification systems are the most popular fuzzy modeling systems used in pattern classification problems. Rule weights...
متن کاملA hybridization of evolutionary fuzzy systems and ant Colony optimization for intrusion detection
A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network security and validated it using the DARPA KDD-Cup99 benchmark data set. The results indicate t...
متن کاملOn Mining Fuzzy Classification Rules for Imbalanced Data
Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...
متن کاملOn Mining Fuzzy Classification Rules for Imbalanced Data
Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...
متن کامل