Compression-Based Induction and Genome Data
Rattikorn Hewett, John H. Leuchner, Choh-Man Teng, Sean D. Mooney, Teri E. Klein · 2002
Our previous work developed SORCER, a learning system that induces a set of rules from a data set repre-sented as a second-order decision table. Second-order decision tables are database relations in which rows have sets of atomic values as components. Using sets of values, which are interpreted as disjunctions, pro-vides compact representations that facilitate efficient management and enhance comprehensibility. SORCER generates classifiers with a near minimum number of rows. The induction algorithm can be viewed as a ta-ble compression technique in which a table of training data is transformed into a second-order table with fewer rows by merging rows in ways that preserve consistency with the training data. In this paper we propose three new mechanisms in SORCER: (1) com-pression by removal of table columns, (2) inclusion of simple rules based on statistics, and (3) a method for partitioning continuous data into discrete clusters. We apply our approach to classify clinical phenotypes of a genetic collagenous disorder, Osteogenesis imperfecta, using a data set of point mutations in COLIA1 gene. Preliminary results show that on the average, over ten 10-fold cross validations, SORCER obtained an error estimate of 16.7 %, compared to 35.1 % obtained from the decision tree learner, C4.5.