Hybrid wrapper-filter approaches for input feature selection using maximum relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)
- Huda, Shamsul, Yearwood, John, Stranieri, Andrew
- Authors: Huda, Shamsul , Yearwood, John , Stranieri, Andrew
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Feature selection is an important research problem in machine learning and data mining applications. This paper proposes a hybrid wrapper and filter feature selection algorithm by introducing the filter's feature ranking score in the wrapper stage to speed up the search process for wrapper and thereby finding a more compact feature subset. The approach hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper approach where Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to guide the search process in the wrapper. The novelty of our approach is that we use hybrid of wrapper and filter methods that combines filter's ranking score with the wrapper-heuristic's score to take advantages of both filter and wrapper heuristics. Performance of the proposed MRANNIGMA has been verified using bench mark data sets and compared to both independent filter and wrapper based approaches. Experimental results show that MR-ANNIGMA achieves more compact feature sets and higher accuracies than both filter and wrapper approaches alone. © 2010 IEEE.
- Authors: Huda, Shamsul , Yearwood, John , Stranieri, Andrew
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Feature selection is an important research problem in machine learning and data mining applications. This paper proposes a hybrid wrapper and filter feature selection algorithm by introducing the filter's feature ranking score in the wrapper stage to speed up the search process for wrapper and thereby finding a more compact feature subset. The approach hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper approach where Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to guide the search process in the wrapper. The novelty of our approach is that we use hybrid of wrapper and filter methods that combines filter's ranking score with the wrapper-heuristic's score to take advantages of both filter and wrapper heuristics. Performance of the proposed MRANNIGMA has been verified using bench mark data sets and compared to both independent filter and wrapper based approaches. Experimental results show that MR-ANNIGMA achieves more compact feature sets and higher accuracies than both filter and wrapper approaches alone. © 2010 IEEE.
Automatic sleep stage identification: difficulties and possible solutions
- Sukhorukova, Nadezda, Stranieri, Andrew, Ofoghi, Bahadorreza, Vamplew, Peter, Saleem, Muhammad Saad, Ma, Liping, Ugon, Adrien, Ugon, Julien, Muecke, Nial, Amiel, Hélène, Philippe, Carole, Bani-Mustafa, Ahmed, Huda, Shamsul, Bertoli, Marcello, Levy, P, Ganascia, J.G
- Authors: Sukhorukova, Nadezda , Stranieri, Andrew , Ofoghi, Bahadorreza , Vamplew, Peter , Saleem, Muhammad Saad , Ma, Liping , Ugon, Adrien , Ugon, Julien , Muecke, Nial , Amiel, Hélène , Philippe, Carole , Bani-Mustafa, Ahmed , Huda, Shamsul , Bertoli, Marcello , Levy, P , Ganascia, J.G
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.
- Authors: Sukhorukova, Nadezda , Stranieri, Andrew , Ofoghi, Bahadorreza , Vamplew, Peter , Saleem, Muhammad Saad , Ma, Liping , Ugon, Adrien , Ugon, Julien , Muecke, Nial , Amiel, Hélène , Philippe, Carole , Bani-Mustafa, Ahmed , Huda, Shamsul , Bertoli, Marcello , Levy, P , Ganascia, J.G
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.
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