Conveners
ISBIS invited session: Advancements in Data-driven Insights
- Daniel Jeske (University of California, Riverside)
Description
ISBIS invited session
SVM Regression Oblique Trees: A Novel Approach to Regression Tasks. This technique combines feature selection based on predictor correlation and a weighted support vector machine classifier with a linear kernel. Evaluation on simulated and real datasets reveals the superior performance of the proposed method compared to other oblique decision tree models, with the added advantage of enhanced...
The focus is on the homogeneity test that evaluates whether two multivariate samples come from the same distribution. The problem arises naturally in various applications, and many methods are available in the literature. Based on data depth, several tests have been proposed for this problem, but they may not be very powerful. In light of the recent development of data depth as an important...
The use of a statistical classifier can be limited by its conditional misclassification rates (i.e., false positive rate and false negative rate) even when the overall misclassification rate is satisfactory. When one or both conditional misclassification rates are high, a neutral zone can be introduced to lower and possibly balance these rates. In this talk the need for neutral zones will be...