A NEW APPROACH BASED ON EVALUATION ALGORITHM FOR CLASSIFICATION PROBLEMS IN DATA MINING

Veri madenciligi, önceden bilinmeyen iliski ve egilimlerin bulunması için büyük miktarlardaki veriyi analiz eden, kullanıcılar için anlamsız bilgiyi anlamlı hale dönüstüren bir yöntemdir. Veri madenciliginde sınıflandırma ise, verilen örneklerden hareket ederek her bir sınıfa iliskin özellikleri bulan ve bu özelliklerin kural cümleleri ile ifade edilmesini saglayan bir yaklasımdır. Bu tezde, veri madenciliginde sınıflandırma kurallarının kesfi için kaba küme yaklasımıyla evrimsel algoritmalara dayanan yeni bir algoritma Rough-Mep algoritması önerilmistir. Rough-Mep algoritmasının etkinligi, klasik makine ögrenimi algoritmaları ve literatürde bulunan algoritmalarla karsılastırılmıs; ikili veya çoklu sınıflı veri kümeleri üzerinde test edilmistir.Data mining is a method for finding unknown relation and trends that analyses great amount of data and transforms insignificant information to significant knowledge for users. Classification in data mining is an approach finding out related attributes of each class and providing display with rule statements from given data sets. In this thesis a new algorithm Rough-Mep algorithm is proposed for discovering of classification rules based on rough set theory and evaluation algorithms. The effectiveness of our approach is tested on eight publicly available binary and n-ary classification data sets

TWO NEW METHOD FOR MULTI CRITERIA STOCHASTIC DECISION MAKING: SMAA-GRA AND SMAA-DEMATEL-GRA

In decision-making in some cases decision makers cannot or do not want to specify preferences with the exact values. To decide with these stochastic data Stochastic Multi-Criteria Acceptability Analysis (SMAA) is an effectively implemented decision support tool. Grey Relational Analysis (GRA) which is working only with deterministic data, is an alternative and a popular method for multi criteria decision making problem. In this study, two new methods SMAA-GRA and SMAA-DEMATEL-GRA are proposed: combination of SMAA-2 and GRA methods and also combination of SMAA-2 and DEMATEL-GRA. The aim of the article is to provide GRA cope with vague and imprecise data in other words, to establish stochastic GRA. And also with DEMATEL we can take into account relationship criteria with each other in decision making process. The proposed methods are applied to both drug benefit risk analysis problem in literature and a real life problem. The study shows that SMAA-GRA and SMAA-DEMATEL-GRA whose results are significant and consistent GRA and DEMATEL methods could be used with ambiguous and arbitrarily distributed data for weights and criteria measurements