146,330 research outputs found
Adapted Branch-and-Bound Algorithm Using SVM With Model Selection
Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.
Identifying Mislabeled Training Data
This paper presents a new approach to identifying and eliminating mislabeled
training instances for supervised learning. The goal of this approach is to
improve classification accuracies produced by learning algorithms by improving
the quality of the training data. Our approach uses a set of learning
algorithms to create classifiers that serve as noise filters for the training
data. We evaluate single algorithm, majority vote and consensus filters on five
datasets that are prone to labeling errors. Our experiments illustrate that
filtering significantly improves classification accuracy for noise levels up to
30 percent. An analytical and empirical evaluation of the precision of our
approach shows that consensus filters are conservative at throwing away good
data at the expense of retaining bad data and that majority filters are better
at detecting bad data at the expense of throwing away good data. This suggests
that for situations in which there is a paucity of data, consensus filters are
preferable, whereas majority vote filters are preferable for situations with an
abundance of data
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