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The presence of a large number of irrelevant features degrades the classifier accuracy, reduces the understanding of data, and increases the overall time needed for training and classification. Hence, Feature selection is a critical step in the machine learning process. The role of feature selection is to select a subset of size ‘d’ (d<n) from the given set of ‘n’ features that leads to the smallest...
Contemporary manufacturing scheduling has still limitations in real-world environments where disturbances on working conditions could occur over time. Therefore, human intervention is required to maintain real-time adaptation and optimization and efficiently adapt to the inherent dynamic of markets. This paper addresses the problem of incorporating rush orders into the current schedule of a manufacturing...
This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic...
We present a new method for the incremental training of multiclass Support Vector Machines that provides computational efficiency for training problems in the case where the training data collection is sequentially enriched and dynamic adaptation of the classifier is required. An auxiliary function that incorporates some desired characteristics in order to provide an upper bound of the objective function...
Measuring the performance of a given classifier is not a straightforward or easy task. Depending on the application, the overall classification rate may not be sufficient if one, or more, of the classes fail in prediction. This problem is also reflected in the feature selection process, especially when a wrapper method is used. Cohen's kappa coefficient is a statistical measure of inter-rater agreement...
Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm is evaluated by its ability to generalize, i.e., to extend this mapping accurately to new data that is commonly referred to as the test data. Good generalization depends crucially on the quality of the training set. Because...
Most dynamic optimizers use feedback-directed adaptive optimization techniques. These techniques are expensive because of the profiling overhead. Although the recent trend has been toward the application of machine learning heuristics in compiler optimization, its role in identification and prediction of hotspots has been ignored. This approach evaluates a support vector machine (SVM) based machine...
A new method for image feature extraction and segmentation is proposed in this paper. Abundant contour feature information of the image is expressed by contourlet transform while texture feature of the image is described by wavelet transform and Gray Level Co-occurrence Matrix (GLCM). The three type feature information compose feature matrix. The presented method describes different image information...
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