بایگانی برچسب برای: support vector machines

A hybrid evolutionary algorithm for attribute selection in data mining[taliem.ir]

A hybrid evolutionary algorithm for attribute selection in data mining

Real life data sets are often interspersed with noise, making the subsequent data mining process difficult. The task of the classifier could be simplified by eliminating attributes that are deemed to be redundant for classification, as the retention of only pertinent attributes would reduce the size of the dataset and subsequently allow more comprehensible analysis of the extracted patterns or rules. In this article, a new hybrid approach comprising of two conventional machine learning algorithms has been proposed to carry out attribute selection. Genetic algorithms (GAs) and support vector machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set by applying the principles of an evolutionary process. The SVM then classifies the patterns in the reduced datasets, corresponding to the attribute subsets represented by the GA chromosomes. The proposed GA- SVM hybrid is subsequently validated using datasets obtained from the UCI machine learning repository. Simulation results demonstrate that the GA-SVM hybrid produces good classification accuracy and a higher level of consistency that is comparable to other established algorithms. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This injects greater diversity and increases the overall fitness of the population. Similarly, the improved mechanism is also validated on the same data sets used in the first stage. The results justify the improvements in the classification accuracy and demonstrate its potential to be a good classifier for future data mining purposes.
Classification of Future Electricity Market Prices[taliem.ir]

Classification of Future Electricity Market Prices

Forecasting short-term electricity market prices has been the focus of several studies in recent years. Although various approaches have been examined, achieving sufficiently low forecasting errors has not been always possible. However, certain applications, such as demand-side management, do not require exact values for future prices but utilize specific price thresholds as the basis for making short-term scheduling decisions. In this paper, classification of future electricity market prices with respect to prespecified price thresholds is introduced. Two alternative models based on support vector machines are proposed in a multi-class, multi-step-ahead price classification context. Numerical results are provided for classifying prices in Ontario’s and Alberta’s markets.
An SVM-Based Solution for Fault Detection in Wind Turbines[taliem.ir]

An SVM-Based Solution for Fault Detection in Wind Turbines

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.