Complexity measure evaluation and selection

Complexity Measure Evaluation and Selection

A Distributed Feature Selection Approach Based on a Complexity Measure

Feature selection is often required as a preliminary step for many machine learning problems. However, most of the existing methods only work in a centralized fashion, i.e. using the whole dataset at once. In this paper we propose a new methodology for distributing the feature selection process by samples which maintains the class distribution. Subsequently, it performs a merging procedure whic...

Feature Selection with Complexity Measure in a Quadratic Programming Setting

Feature selection is a topic of growing interest mainly due to the increasing amount of information, being an essential task in many machine learning problems with high dimensional data. The selection of a subset of relevant features help to reduce the complexity of the problem and the building of robust learning models. This work presents an adaptation of a recent quadratic programming feature...

Assessing tobacco dependence: a guide to measure evaluation and selection.

Tobacco dependence is a key construct in tobacco research. This paper describes a construct validation approach to dependence assessment and describes key conceptual and psychometric criteria on which to evaluate putative measures of dependence. Five current dependence scales-the Fagerström Test for Nicotine Dependence; the Diagnostic and Statistical Manual of Mental Disorders (4th ed.); the Ci...

Generalized Statistical Complexity Measure

OSVALDO A. ROSSO Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine and Hunter Medical Research Institute, School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón II, Ciudad Universitaria, 1...