Feature selection for high dimensional imbalanced class data using harmony search

Misclassification costs of minority class data in real-world applications can be very high. This is a challenging problem especially when the data is also high in dimensionality because of the increase in overfitting and lower model interpretability. Feature selection is recently a popular way to address this problem by identifying features that best predict a minority class. This paper introduces a novel feature selection method call SYMON which uses symmetrical uncertainty and harmony search. Unlike existing methods, SYMON uses symmetrical uncertainty to weigh features with respect to their dependency to class labels. This helps to identify powerful features in retrieving the least frequent class labels. SYMON also uses harmony search to formulate the feature selection phase as an optimisation problem to select the best possible combination of features. The proposed algorithm is able to deal with situations where a set of features have the same weight, by incorporating two vector tuning operations embedded in the harmony search process. In this paper, SYMON is compared against various benchmark feature selection algorithms that were developed to address the same issue. Our empirical evaluation on different micro-array data sets using G-Mean and AUC measures confirm that SYMON is a comparable or a better solution to current benchmarks

Parameter-free classification in multi-class imbalanced data sets

International audienceMany applications deal with classification in multi-class imbalanced contexts. In such difficult situations, classical CBA-like approaches (Classification Based on Association rules) show their limits. Most CBA-like methods actually are One-Vs-All approaches (OVA), i.e., the selected classification rules are relevant for one class and irrelevant for the union of the other classes. In this paper, we point out recurrent problems encountered by OVA approaches applied to multi-class imbalanced data sets (e.g., improper bias towards majority classes, conflicting rules). That is why we propose a new One-Versus-Each (OVE) framework. In this framework, a rule has to be relevant for one class and irrelevant for every other class taken separately. Our approach, called fitcare, is empirically validated on various benchmark data sets and our theoretical findings are confirmed

Parameter-free classification in multi-class imbalanced data sets

International audienceMany applications deal with classification in multi-class imbalanced contexts. In such difficult situations, classical CBA-like approaches (Classification Based on Association rules) show their limits. Most CBA-like methods actually are One-Vs-All approaches (OVA), i.e., the selected classification rules are relevant for one class and irrelevant for the union of the other classes. In this paper, we point out recurrent problems encountered by OVA approaches applied to multi-class imbalanced data sets (e.g., improper bias towards majority classes, conflicting rules). That is why we propose a new One-Versus-Each (OVE) framework. In this framework, a rule has to be relevant for one class and irrelevant for every other class taken separately. Our approach, called fitcare, is empirically validated on various benchmark data sets and our theoretical findings are confirmed