Using hybrid associative classifier with translation (HACT) for studying imbalanced data sets

作者：Cleofas Sanchez Laura; Guzman Escobedo M; Valdovinos Rosas Rosa Maria; Yanez Marquez Cornelio; Camacho Nieto Oscar

来源：Ingenieria e Investigacion, 2012, 32(1): 53-57.

DOI：10.15446/ing.investig.v32n1.28522

• data set
• associative model
• under sampling
• class imbalance
• pre-processing

Class imbalance may reduce the classifier performance in several recognition pattern problems. Such negative effect is more notable with least represented class (minority class) Patterns. A strategy for handling this problem consisted of treating the classes included in this problem separately (majority and minority classes) to balance the data sets (DS). This paper has studied high sensitivity to class imbalance shown by an associative model of classification: hybrid associative classifier with translation (HACT); imbalanced DS impact on associative model performance was studied. The convenience of using sub-sampling methods for decreasing imbalanced negative effects on associative memories was analysed. This proposal%26apos;s feasibility was based on experimental results obtained from eleven real-world datasets.

• 出版日期2012-4