Learning Timbre Analogies from Unlabelled Data by Multivariate Tree Regression

This is the Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the Journal of New Music Research, November 2011, copyright Taylor & Francis. The published article is available online at http://www.tandfonline.com/10.1080/09298215.2011.596938

What Matter for Child Development?

This paper estimates production functions of child cognitive and social development using a panel data of nine-year old children each with over two hundred home and school inputs as well as family background variables. A tree regression method is used to conduct estimation under various speci.cations. A small subset of inputs is found consistently important in explaining variances of child development results, including the number of books a child has at various ages and how often a mother reads to child by age .ve, while the eects of race and maternal employment are negligible when detailed inputs are controlled.child development, tree regression method, panel data

What Matter for Child Development?

This paper estimates production functions of child cognitive and social development using a panel data of nine-year old children each with over two hundred home and school inputs as well as family background variables. A tree regression method is used to conduct estimation under various specifications. A small subset of inputs is found consistently important in explaining variances of child development results, including the number of books a child has at various ages and how often a mother reads to child by age five, while the effects of race and maternal employment are negligible when detailed inputs are controlled.child development, tree regression method, panel data inequality, economic development

A General Framework for Fair Regression

Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints in kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision tree regression. Further, we focus on examining the effect of incorporating these constraints in decision tree regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques. We show that the order of complexity of memory and computation is preserved for such models and tightly bound the expected perturbations to the model in terms of the number of leaves of the trees. Importantly, the approach works on trained models and hence can be easily applied to models in current use and group labels are only required on training data.Comment: 8 pages, 4 figures, 2 pages reference

Machine learning reveals orbital interaction in crystalline materials

We propose a novel representation of crystalline materials named orbital-field matrix (OFM) based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Our experiment shows that the formation energies of crystalline materials, the atomization energies of molecular materials, and the local magnetic moments of the constituent atoms in transition metal--rare-earth metal bimetal alloys can be predicted with high accuracy using the OFM. Knowledge regarding the role of coordination numbers of transition-metal and rare-earth metal elements in determining the local magnetic moment of transition metal sites can be acquired directly from decision tree regression analyses using the OFM.Comment: 10 page

Understanding Preferences For Income Redestribution

Recent research suggests that income redistribution preferences vary across identity groups. We employ a new pattern recognition technology, tree regression analysis, to uncover what these groups are. Using data from the General Social Survey, we present a new stylized fact that preferences for governmental provision of income redistribution vary systematically with race, gender, and class background. We explore the extent to which existing theories of income redistribution can explain our results, but conclude that current approaches do not fully explain the findings.

Tree regression models using statistical testing and mixed integer programming

Regression analysis is a statistical procedure that fits a mathematical function to a set of data in order to capture the relationship between dependent and independent variables. In tree regression, tree structures are constructed by repeated splits of the input space into two subsets, creating if-then-else rules. Such models are popular in the literature due to their ability to be computed quickly and their simple interpretations. This work introduces a tree regression algorithm that exploits an optimisation model of an existing literature method called Mathematical Programming Tree (MPtree) to optimally split nodes into subsets and applies a statistical test to assess the quality of the partitioning. Additionally, an approach of splitting nodes using multivariate decision rules is explored in this work and compared in terms of performance and computational efficiency. Finally, a novel mathematical model is introduced that performs subset selection on each node in order to select an optimal set of variables to considered for splitting, that improves the computational performance of the proposed algorithm