Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets
نویسندگان
چکیده
منابع مشابه
Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets
In many real application areas, the data used are highly skewed and the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this paper is to improve the performance of fuzzy rule based classification systems on imb...
متن کاملA Genetic Algorithm for Feature Selection and Granularity Learning in Fuzzy Rule-Based Classification Systems for Highly Imbalanced Data-Sets
This contribution proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy RuleBased Classification Systems in the scenario of data-sets with a high imbalance degree. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more comp...
متن کاملFeature Selection and Granularity Learning in Genetic Fuzzy Rule-Based Classification Systems for Highly Imbalanced Data-Sets
This paper proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of highly imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more compact models by sel...
متن کاملMulti-class Imbalanced Data-Sets with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning
In a classification task, the imbalance class problem is present when the data-set has a very different distribution of examples among their classes. The main handicap of this type of problem is that standard learning algorithms consider a balanced training set and this supposes a bias towards the majority classes. In order to provide a correct identification of the different classes of the pro...
متن کاملOn the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets
When performing a classification task, we may find some data-sets with a different class distribution among their patterns. This problem is known as classification with imbalanced data-sets and it appears in many real application areas. For this reason, it has recently become a relevant topic in the area of Machine Learning. The aim of this work is to improve the behaviour of fuzzy rule based c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2009
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2008.11.004