Search results for "Evaluation function"
showing 5 items of 5 documents
Local Feature Selection with Dynamic Integration of Classifiers
2000
Multidimensional data is often feature space heterogeneous so that individual features have unequal importance in different sub areas of the feature space. This motivates to search for a technique that provides a strategic splitting of the instance space being able to identify the best subset of features for each instance to be classified. Our technique applies the wrapper approach where a classification algorithm is used as an evaluation function to differentiate between different feature subsets. In order to make the feature selection local, we apply the recent technique for dynamic integration of classifiers. This allows to determine which classifier and which feature subset should be us…
Scheduling in a continuous galvanizing line
2009
In this paper we address a sequencing problem in a Continuous Galvanizing Line of a Spanish Steel Company. Production scheduling in this context is an extremely complex task which needs to take into account many constraints. We present a conceptually simple model and a Tabu Search (TS) algorithm that efficiently solves it. The TS moves are defined in order to repair non-satisfied constraints, leading to smaller and more efficient neighbourhoods. The TS co-ordinates several intensification and diversification procedures guided by an evaluation function based on a shifting penalty strategy. This function reinforces the anticycling mechanism and makes the algorithm avoid already visited soluti…
Fuzzified Game Tree Search – Precision vs Speed
2012
Most game tree search algorithms consider finding the optimal move. That is, given an evaluation function they guarantee that selected move will be the best according to it. However, in practice most evaluation functions are themselves approximations and cannot be considered "optimal". Besides, we might be satisfied with nearly optimal solution if it gives us a considerable performance improvement. In this paper we present the approximation based implementations of the fuzzified game tree search algorithm. The paradigm of the algorithm allows us to efficiently find nearly optimal solutions so we can choose the "target quality" of the search with arbitrary precision --- either it is 100% (pr…
Ensemble Feature Selection Based on the Contextual Merit
2001
Recent research has proved the benefits of using ensembles of classifiers for classification problems. Ensembles constructed by machine learning methods manipulating the training set are used to create diverse sets of accurate classifiers. Different feature selection techniques based on applying different heuristics for generating base classifiers can be adjusted to specific domain characteristics. In this paper we consider and experiment with the contextual feature merit measure as a feature selection heuristic. We use the diversity of an ensemble as evaluation function in our new algorithm with a refinement cycle. We have evaluated our algorithm on seven data sets from UCI. The experiment…
A Modified Tabu Thresholding Approach for the Generalised Restricted Vertex Colouring Problem
1996
We present a modification of the Tabu Thresholding (TT) approach and apply it to the solution of the generalised restricted vertex colouring problem. Both the bounded and unbounded cases are treated. In our algorithms, the basic TT elements are supplemented with an evaluation function that depends on the best solution obtained so far, together with a mechanism which reinforces the aggressive search in the improving phase, and new diversification strategies which depend on the state of the search. The procedure is illustrated through the solution of the problem of minimising the number of workers in a heterogeneous workforce.