Fuzzy Mutual Information Feature Selection Based on Representative Samples

Fuzzy Mutual Information Feature Selection Based on Representative Samples

Omar A. M. Salem (Faculty of Computer Science and Informatics, Suez Canal University, Ismailia, Egypt) and Liwei Wang (International School of Software, Wuhan University, Wuhan, China)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJSI.2018010105
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Building classification models from real-world datasets became a difficult task, especially in datasets with high dimensional features. Unfortunately, these datasets may include irrelevant or redundant features which have a negative effect on the classification performance. Selecting the significant features and eliminating undesirable features can improve the classification models. Fuzzy mutual information is widely used feature selection to find the best feature subset before classification process. However, it requires more computation and storage space. To overcome these limitations, this paper proposes an improved fuzzy mutual information feature selection based on representative samples. Based on benchmark datasets, the experiments show that the proposed method achieved better results in the terms of classification accuracy, selected feature subset size, storage, and stability.
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Nowadays, classification models have various applications in many areas such as medical, business, engineering, life and social sciences. As the size of real-world datasets from these areas continues to increase, building classification models become a significantly more difficult task (Janecek et al., 2008). Although high-dimensional data include important features, it may also include undesirable data such as irrelevant and redundant features. The presence of undesirable features leads to a decrease in classification accuracy (Dash and Liu, 2003; Vieira et al., 2012). Moreover, it increases storage space and memory usage (Dash and Liu, 2003; Janecek et al., 2008). So, selecting relevant features and eliminating irrelevant or redundant features helps to build effective classification models (Yu et al., 2011).

Features selection as a preprocessing step aims to select the minimum subset that describes the data efficiently and increases the classification accuracy (Guyon and Elisseeff, 2003). It can be grouped into a wrapper, filter, and embedded approaches. Both wrapper and embedded approaches can be considered as classifier-dependent feature selection, while filter approaches can be considered as a classifier-independent feature selection (Bennasar et al., 2015). In this study, we use filter approach according to its advantages over wrapper or embedded approaches. The main advantages of filter approaches are classifier-independent, less time consuming and more practical for classification models (Saeys et al., 2007).

Filter approaches try to filter undesirable features out before classification process (Garc´ıa et al., 2015). They select the highly ranked features based on characteristics of the training data (Guyon and Elisseeff, 2003). The main characteristics of data depend on two relations: relevance and redundancy (Chandrashekar and Sahin, 2014). Relevance describes how the features can discriminate the different classes, while redundancy describes how the features depend on each other. So maximizing feature relevance and minimizing feature redundancy leads to best feature ranking. To evaluate the characteristics of features, filter approach uses many evaluation measures such as correlation (Hall, 1999), Shannon mutual information (Vergara and Est´evez, 2014). Correlation measures are suitable only for a linear relationship among features, while Shannon mutual information is suitable for linear and non-linear relations among features (Lee et al., 2012). However, Shannon mutual information has some limitations: First, it requires discretization step before dealing with continuous data. But, it is difficult to avoid information loss results from discretization (Ching et al., 1995; Shen and Jensen, 2004). Second, it depends only on the inner-class information without considering outer-class information (Liang et al., 2002).

To overcome these limitations, various algorithms based on mutual information with fuzzification has been introduced in many literatures. Yu et al. (2011). proposed a fuzzy mutual information using logarithmic concept. Another algorithm was proposed to estimate a fuzzy mutual information using complement instead of logarithmic concept (Zhao et al., 2015). Both of fuzzy mutual information algorithms depend on the fuzzy binary relation. This relation can be represented in relation matrix. The size of relation matrix depends on the number of samples in the input feature. Each row or column in the relation matrix represents the relation between one sample and each of the remaining samples. So, estimating relation matrix requires more storage and computational time, especially for datasets with a tremendous amount of samples (Yu et al., 2007). Motivated by these limitations of fuzzy mutual information, we proposed a new estimation of relation matrix. To create this matrix, we estimated the relation between one sample and representative samples. These samples consist of the averages of data samples belonging to the same class. Using representative samples instead of all samples can reduce the size of relation matrix.

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