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An approach to unsupervised classification

Przybyła T., Pander T., Horoba K., Kupka T., Matonia A.

Journal of Medical Informatics & Technologies

2011

Vol. 17

105--111

Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method.

Application of a genetic algorithm for the credit risk assessment problem

Marinaki M., Marinakis Y., Zopounidis C.

Foundations of Computing and Decision Sciences

2007

Vol. 32, No. 2

139-152

In this paper, a new procedure that utilizes a Genetic algorithm in order to solve the Feature Subset Selection problem is presented. The proposed algorithm is combined with a number of nearest neighbor based classifiers. The proposed Genetic based classification algorithm is applied for the solution of the Credit Risk Assessment Classification problem. The performance of the algorithm is tested using data from 1411 firms derived from the loan portfolio of a leading Greek Commercial Bank in order to classify the firms in different groups representing different levels of credit risk. A Comparison of the algorithm with other classification methods, such as SVM, CART is performed using these data. The algorithm is, also, compared with another metaheuristic algorithm. In this algorithm, the feature subset selection problem is solved using Tabu Search and in the classification phase the Nearest Neighbor Classifier is used. The results obtained using the genetic algorithm for the credit risk assessment classification problem are better than the results of all other classification methods and the metaheuristic algorithm used for the comparisons in this paper.