HANDLING MISSING ATTRIBUTE VALUES IN DECISION TABLES USING VALUED TOLERANCE APPROACH

Rule induction is one of the key areas in data mining as it is applied to a large number of real life data. However, in such real life data, the information is incompletely specified most of the time. To induce rules from these incomplete data, more powerful algorithms are necessary. This research work mainly focuses on a probabilistic approach based on the valued tolerance relation. This thesis is divided into two parts. The first part describes the implementation of the valued tolerance relation. The induced rules are then evaluated based on the error rate due to incorrectly classified and unclassified examples. The second part of this research work shows a comparison of the rules induced by the MLEM2 algorithm that has been implemented before, with the rules induced by the valued tolerance based approach which was implemented as part of this research. Hence, through this thesis, the error rate for the MLEM2 algorithm and the valued tolerance based approach are compared and the results are documented

A comparison of sixteen classification strategies of rule induction from incomplete data using the MLEM2 algorithm

In data mining, rule induction is a process of extracting formal rules from decision tables, where the later are the tabulated observations, which typically consist of few attributes, i.e., independent variables and a decision, i.e., a dependent variable. Each tuple in the table is considered as a case, and there could be n number of cases for a table specifying each observation. The efficiency of the rule induction depends on how many cases are successfully characterized by the generated set of rules, i.e., ruleset. There are different rule induction algorithms, such as LEM1, LEM2, MLEM2. In the real world, datasets will be imperfect, inconsistent, and incomplete. MLEM2 is an efficient algorithm to deal with such sorts of data, but the quality of rule induction largely depends on the chosen classification strategy. We tried to compare the 16 classification strategies of rule induction using MLEM2 on incomplete data. For this, we implemented MLEM2 for inducing rulesets based on the selection of the type of approximation, i.e., singleton, subset or concept, and the value of alpha for calculating probabilistic approximations. A program called rule checker is used to calculate the error rate based on the classification strategy specified. To reduce the anomalies, we used ten-fold cross-validation to measure the error rate for each classification. Error rates for the above strategies are being calculated for different datasets, compared, and presented