748,954 research outputs found
Branch and bound method for regression-based controlled variable selection
Self-optimizing control is a promising method for selection of controlled variables (CVs) from available measurements. Recently, Ye, Cao, Li, and Song (2012) have proposed a globally optimal method for selection of self-optimizing CVs by converting the CV selection problem into a regression problem. In this approach, the necessary conditions of optimality (NCO) are approximated by linear combinations of available measurements over the entire operation region. In practice, it is desired that a subset of available measurements be combined as CVs to obtain a good trade-off between the economic performance and the complexity of control system. The subset selection problem, however, is combinatorial in nature, which makes the application of the globally optimal CV selection method to large-scale processes difficult. In this work, an efficient branch and bound (BAB) algorithm is developed to handle the computational complexity associated with the selection of globally optimal CVs. The proposed BAB algorithm identifies the best measurement subset such that the regression error in approximating NCO is minimized and is also applicable to the general regression problem. Numerical tests using randomly generated matrices and a binary distillation column case study demonstrate the computational efficiency of the proposed BAB algorithm
A note on sparse least-squares regression
We compute a \emph{sparse} solution to the classical least-squares problem
where is an arbitrary matrix. We describe a novel
algorithm for this sparse least-squares problem. The algorithm operates as
follows: first, it selects columns from , and then solves a least-squares
problem only with the selected columns. The column selection algorithm that we
use is known to perform well for the well studied column subset selection
problem. The contribution of this article is to show that it gives favorable
results for sparse least-squares as well. Specifically, we prove that the
solution vector obtained by our algorithm is close to the solution vector
obtained via what is known as the "SVD-truncated regularization approach".Comment: Information Processing Letters, to appea
Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World
We address the two fundamental problems of spatial field reconstruction and
sensor selection in heterogeneous sensor networks: (i) how to efficiently
perform spatial field reconstruction based on measurements obtained
simultaneously from networks with both high and low quality sensors; and (ii)
how to perform query based sensor set selection with predictive MSE performance
guarantee. For the first problem, we developed a low complexity algorithm based
on the spatial best linear unbiased estimator (S-BLUE). Next, building on the
S-BLUE, we address the second problem, and develop an efficient algorithm for
query based sensor set selection with performance guarantee. Our algorithm is
based on the Cross Entropy method which solves the combinatorial optimization
problem in an efficient manner.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the
Developing Worl
Full model selection in the space of data mining operators
We propose a framework and a novel algorithm for the full model selection (FMS) problem. The proposed algorithm, combining both genetic algorithms (GA) and particle swarm optimization (PSO), is named GPS (which stands for GAPSO-FMS), in which a GA is used for searching the optimal structure of a data mining solution, and PSO is used for searching the optimal parameter set for a particular structure instance. Given a classification or regression problem, GPS outputs a FMS solution as a directed acyclic graph consisting of diverse data mining operators that are applicable to the problem, including data cleansing, data sampling, feature transformation/selection and algorithm operators. The solution can also be represented graphically in a human readable form. Experimental results demonstrate the benefit of the algorithm
Algorithm Selection Framework: A Holistic Approach to the Algorithm Selection Problem
A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of the top ranked recommendation is considered excellent with at least 95 percent of the best observed recall; the average of this metric is 79 percent due to two poorly performing recommendations. The top recommendation is Pareto efficient for three of the problems. The framework measures well against an a-priori set of criteria. The framework provides value by filtering the candidate of analytic techniques and, often, selecting a high performing technique as the top ranked recommendation. The user input and meta-data used by the framework contain information with high potential for effective algorithm selection. Future work should optimize the recommendation logic and expand the scope of techniques for other types of analysis problems. Further, the results of this proposed study should be leveraged in order to better understand the behavior of meta-learning models
ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a set of
algorithms on a per-instance basis in order to exploit the varying performance
of algorithms over a set of instances. The algorithm selection problem is
attracting increasing attention from researchers and practitioners in AI. Years
of fruitful applications in a number of domains have resulted in a large amount
of data, but the community lacks a standard format or repository for this data.
This situation makes it difficult to share and compare different approaches
effectively, as is done in other, more established fields. It also
unnecessarily hinders new researchers who want to work in this area. To address
this problem, we introduce a standardized format for representing algorithm
selection scenarios and a repository that contains a growing number of data
sets from the literature. Our format has been designed to be able to express a
wide variety of different scenarios. Demonstrating the breadth and power of our
platform, we describe a set of example experiments that build and evaluate
algorithm selection models through a common interface. The results display the
potential of algorithm selection to achieve significant performance
improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa
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