Amended Fused Topsis-Vikor For Classification (Atovic) Applied To Some Uci Data Sets

Classification procedure is an important task of expert and intelligent systems. Developing new algorithms of classification which improve accuracy or true positive rates could have an influence on some life problems such as diagnosis prediction in medical domain. Multi-criteria decision making (MCDM) methods are expected to search the best alternative according to some criteria. Each criterion has a value relative to each alternative. There are only two sets: a set of criteria and a set of alternatives. This work merges MCDM methods TOPSIS and VIKOR and modifies them to be used for classification where the used sets are three: the classes, the objects and the attributes (features) describing the objects. Hence, ATOVIC, a new classification algorithm is proposed. In ATOVIC, criteria are replaced by features and alternatives are replaced by objects. The latter belong to corresponding classes. Two sets are employed one serves as reference and second serves as test. An object from test set will be classified to the relative class based on the reference set. ATOVIC is applied on a benchmark (UCI) CLEVELAND data set to predict heart disease. Following the complexity of the data set and its importance, ATOVIC application is done on different test sets of CLEVELAND using binary classification and multi-classification. Moreover, ATOVIC is applied to thyroid data set to detect hyperthyroidism and hypothyroidism diseases. The obtained results show the efficiency of ATOVIC in medical domain. In addition, ATOVIC is applied to three other data sets: chess, nursery and titanic, from UCI and KEEL websites. The obtained results are compared to those of some classifiers from literature. The experimental results demonstrate that ATOVIC method improves accuracy and true positive rates comparing to most classifiers considered from literature. Hence ATOVIC is promising for use in prediction or classification. (C) 2018 Elsevier Ltd. All rights reserved.