Hybrid wrapper-filter approaches for input feature selection using maximum relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)
- Authors: Huda, Shamsul , Yearwood, John , Stranieri, Andrew
- Date: 2010
- Type: Text , Conference proceedings
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- 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.
Predicting Australian stock market index using neural networks exploiting dynamical swings and intermarket influences
- Authors: Pan, Heping , Tilakaratne, Chandima , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 43-55
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- Reviewed:
- Description: This paper presents a computational approach for predicting the Australian stock market index AORD using multi-layer feed-forward neural networks front the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network, or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets,. and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period; the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily, returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness.
- Description: C1
- Description: 2003001440