A survey on multi-output regression

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This paper provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks

Marketing, cooperatives and price heterogeneity: evidence from the CIS dairy sector

Drawing on survey data, this paper identifies the determinants of variations in farm gate milk prices for three CIS countries (Armenia, Moldova and Ukraine). We apply a multi-level modeling approach, specifically a bootstrapped mixed-effects linear regression model. The analysis suggests three main strategies to improve the price received by farmers for their output: consolidation, competition for output and stable supply chain relationships. In Armenia and Ukraine selling through a marketing cooperative has a significant, positive, albeit modest, effect on farm gate milk prices. In all three countries studied, the size of dairy operations, trust and contracting also affect positively the prices received by farmers.price heterogeneity, milk, cooperatives, Armenia, Moldova, Ukraine, Agribusiness, Demand and Price Analysis, Marketing, O13, P32, Q13,

Discrete action control for prosthetic digits

We aim to develop a paradigm for simultaneous and independent control of multiple degrees of freedom (DOFs) for upper-limb prostheses. To that end, we introduce action control, a novel method to operate prosthetic digits with surface electromyography (EMG) based on multi-output, multi-class classification. At each time step, the decoder classifies movement intent for each controllable DOF into one of three categories: open, close, or stall (i.e., no movement). We implemented a real-time myoelectric control system using this method and evaluated it by running experiments with one unilateral and two bilateral amputees. Participants controlled a six-DOF bar interface on a computer display, with each DOF corresponding to a motor function available in multi-articulated prostheses. We show that action control can significantly and systematically outperform the state-of-the-art method of position control via multi-output regression in both task- and non-task-related measures. Using the action control paradigm, improvements in median task performance over regression-based control ranged from 20.14% to 62.32% for individual participants. Analysis of a post-experimental survey revealed that all participants rated action higher than position control in a series of qualitative questions and expressed an overall preference for the former. Action control has the potential to improve the dexterity of upper-limb prostheses. In comparison with regression-based systems, it only requires discrete instead of real-valued ground truth labels, typically collected with motion tracking systems. This feature makes the system both practical in a clinical setting and also suitable for bilateral amputation. This work is the first demonstration of myoelectric digit control in bilateral upper-limb amputees. Further investigation and pre-clinical evaluation are required to assess the translational potential of the method

Determinants of Output Market Participation by Smallholder Farmers in Upper Guruve District, Zimbabwe

Market participation by smallholder farmers in Sub-Saharan Africa is characteristically low, with most of these farmers having limited access to both input and output markets. This paper investigates the factors associated with output market participation by smallholder farmers in Upper Guruve District, Zimbabwe. A multi-stage sampling technique was used to select 200 households for the survey, with the collected primary data analyzed using STATA version 15 through a Multinomial Logistic regression model. The key determinants to output market participation included gender, age and experience, area cropped to soyabean, input and output market prices, profitability and access to market and extension support services. The study demonstrates the need for market information dissemination so as to promote and increase soyabean productivity. Government intervention should also focus on providing an enabling environment for improved private sector involvement and gender empowerment since women are currently marginalized in soyabean production and market participation. This paper is critical for guiding policy-making and development of strategies to increase soya production for the benefit of both farmers and the economy in general. Keywords: Soyabean, Output Market Participation, Smallholder Farmers, Zimbabwe DOI: 10.7176/JESD/12-2-02 Publication date: January 31st 202

Expert System-Based Exploratory Approach to Cost Modeling of Reinforced Concrete Office Building

Expert system is a conventional method that is in use in cost modeling, considering its advantage over traditional regression method. It is based on this fact, that this study aimed at deploying neural network in cost modeling of reinforced concrete office building. One hundred (100) samples were selected at random and divided into two; one part was used to develop network algorithm while the second part was used for model validation. Neural network was used to generate the model algorithm; the model is divided into 3 modules: the data optimization module, criteria selection with initializing and terminating modules. Regression analysis was carried out and model validated with Jackknife re-sampling technique. The colinearity analysis indicates high level of tolerance and -0.07403 lowest variation prediction quotients to 0.66639 highest variation quotients. Also the Regression coefficient (R-square) value for determining the model fitness is 0.034 with standard error of 0.048 this attest to the fitness of the model generated. The model is flexible in accommodating new data and variables, thus, it allows for regular updating

Photometric redshift estimation based on data mining with PhotoRApToR

Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multilayer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and postprocessing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.Comment: To appear on Experimental Astronomy, Springer, 20 pages, 15 figure