Multi-label LeGo — Enhancing Multi-label Classifiers with Local Patterns ∗

Abstract. The straightforward approach to multi-label classification is based on decomposition, which essentially treats all labels independently and ignores interactions between labels. We propose to enhance multilabel classifiers with features constructed from local patterns representing explicitly such interdependencies. An Exceptional Model Mining instance is employed to find local patterns representing parts of the data where the conditional dependence relations between the labels are exceptional. We construct binary features from these patterns that can be interpreted as partial solutions to local complexities in the data. These features are then used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of decompositive multi-label learning techniques. Keywords: Exceptional Model Mining; Multi-Label Classification; LeGo

Prevention of colorectal cancer by combining early detection and chemoprevention

Colorectal cancer is a major cause of cancer-related mortality. Most colorectal cancers arise in precursor lesions over a number of years. The carcinogenetic pathway is modulated by a wide array of genetic and epigenetic factors. Two major approaches to halting carcinogenesis are chemoprevention and early detection. Chemoprevention is an attractive emerging option-advances in understanding the carcinogenetic pathways and progress in evaluating various putative agents are promising. However, several issues regarding efficacy, safety, and acceptability require answers before an ideal agent can come into widespread use. Early detection and removal of precursor lesions show promise in reducing disease burden. Although the concept has been around and has been widely advocated for several years, participation in screening remains low. The ideal test in terms of practicality, accuracy, and safety is still debated. This article reviews the recent literature related to screening and chemoprevention of colorectal cancer. Copyright © 2009 by Current Medicine Group LLC