Greedy algorithm

About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.

A Fast Algorithm For The Greedy Interchange For Large-Scale Clustering And Median Location Problems

Abstract: A fast algorithm is described for implementing the greedy interchange heuristic for lise in solving large scale clustering and uncapacitated median location problems. Computational experience is reported for these algorithms on a number of large randomly generated networks and on some difficult problem sets, and comparisons with some other implementations are made. An additional heuristic is proposed for solving these set partitioning problems based on an efficient procedure for achieving the interchange.

On the Consistency of Feature Selection using Greedy Least Squares Regression

Abstract: This paper studies the feature selection problem using a greedy least squares regression algorithm. We show that under a certain irrepresentable condition on the design matrix (but independent of the sparse target), the greedy algorithm can select features consistently when the sample size approaches infinity. The condition is identical to a corresponding condition for Lasso. Moreover, under a sparse eigenvalue condition, the greedy algorithm can reliably identify features as long as each nonzero coefficient is larger than a constant times the noise level. In comparison, Lasso may require the coefficients to be larger than O(√s) times the noise level in the worst case, where s is the number of nonzero coefficients.

A GRASP approach to the container-loading problem

Abstract: The container-loading problem aims to determine the arrangement of items in a container. We present GRMODGRASP, a new algorithm for the CLP based on the GRASP (greedy randomized adaptive search procedure) paradigm. We evaluate GRMODGRASP'S performance in terms of volume use and load stability and by comparing it with nine well-known algorithms. Our approach produces solutions that surpass other approaches' solutions in terms of volume use and cargo stability.

Induction of decision trees using relieff

Abstract: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies between then Greedy search prevents current inductive machine learning algorithms to detect significant dependencies between the attributes Recently, Kira and Rendell developed the RELIEF algorithm for estimating the quality of attributes that is able to detect dependencies between attributes We show strong relation between RELIEF’s estimates and impurity functions, that are usually used for heuristic guidance of inductive learning algorithms We propose to use RELIEFF, an extended version of RELIEF, instead of myopic impurity functions We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step The algorithm is tested on several artificial and several real world problems Results show the advantage of the presented approach to inductive learning and open a wide range of possibilities for using RELIEFF