Intelligent Data Engineering and Automated Learning 

About: Intelligent Data Engineering and Automated Learning is an academic conference. The conference publishes majorly in the area(s): Cluster analysis & Artificial neural network. Over the lifetime, 1887 publications have been published by the conference receiving 12350 citations.

Filter methods for feature selection: a comparative study

Abstract: Adequate selection of features may improve accuracy and efficiency of classifier methods. There are two main approaches for feature selection: wrapper methods, in which the features are selected using the classifier, and filter methods, in which the selection of features is independent of the classifier used. Although the wrapper approach may obtain better performances, it requires greater computational resources. For this reason, lately a new paradigm, hybrid approach, that combines both filter and wrapper methods has emerged. One of its problems is to select the filter method that gives the best relevance index for each case, and this is not an easy to solve question. Different approaches to relevance evaluation lead to a large number of indices for ranking and selection. In this paper, several filter methods are applied over artificial data sets with different number of relevant features, level of noise in the output, interaction between features and increasing number of samples. The results obtained for the four filters studied (ReliefF, Correlation-based Feature Selection, Fast Correlated Based Filter and INTERACT) are compared and discussed. The final aim of this study is to select a filter to construct a hybrid method for feature selection.

Support Vector Machine Regression for Volatile Stock Market Prediction

Abstract: Recently, Support Vector Regression (SVR) has been introduced to solve regression and prediction problems. In this paper, we apply SVR to financial prediction tasks. In particular, the financial data are usually noisy and the associated risk is time-varying. Therefore, our SVR model is an extension of the standard SVR which incorporates margins adaptation. By varying the margins of the SVR, we could reflect the change in volatility of the financial data. Furthermore, we have analyzed the effect of asymmetrical margins so as to allow for the reduction of the downside risk. Our experimental results show that the use of standard deviation to calculate a variable margin gives a good predictive result in the prediction of Hang Seng Index.

Active learning for regression based on query by committee

Abstract: We investigate a committee-based approach for active learning of real-valued functions. This is a variance-only strategy for selection of informative training data. As such it is shown to suffer when the model class is misspecified since the learner's bias is high. Conversely, the strategy outperforms passive selection when the model class is very expressive since active minimization of the variance avoids overfitting.

Distance Weighted Cosine Similarity Measure for Text Classification

Abstract: In Vector Space Model, Cosine is widely used to measure the similarity between two vectors. Its calculation is very efficient, especially for sparse vectors, as only the non-zero dimensions need to be considered. As a fundamental component, cosine similarity has been applied in solving different text mining problems, such as text classification, text summarization, information retrieval, question answering, and so on. Although it is popular, the cosine similarity does have some problems. Starting with a few synthetic samples, we demonstrate some problems of cosine similarity: it is overly biased by features of higher values and does not care much about how many features two vectors share. A distance weighted cosine similarity metric is thus proposed. Extensive experiments on text classification exhibit the effectiveness of the proposed metric.

Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model

Abstract: We have explored different methods of improving the accuracy of a Naive Bayes classifier for sentiment analysis. We observed that a combination of methods like effective negation handling, word n-grams and feature selection by mutual information results in a significant improvement in accuracy. This implies that a highly accurate and fast sentiment classifier can be built using a simple Naive Bayes model that has linear training and testing time complexities. We achieved an accuracy of 88.80% on the popular IMDB movie reviews dataset. The proposed method can be generalized to a number of text categorization problems for improving speed and accuracy.