One of the difficult challenges facing data miners is that algorithm performance degrades if the feature space contains redundant or irrelevant features. Therefore, as a critical preprocess task, dimension reduction is used to build a smaller space containing valuable features. There are 2 different approaches for dimension reduction: feature extraction and feature selection, which itself is divided into wrapper and filter approaches. In high-dimensional spaces, feature extrac-
Nowadays, text is one prevalent forms of data and text classification is a widely used data mining task, which has various application fields. One mass-produced instance of text is email. As a communication medium, despite having a lot of advantages, email suffers from a serious problem. The number of spam emails has steadily increased in the recent years, leading to considerable irritation. Therefore, spam detection has emerged as a separate field of text classification. A primary challenge of text classification, which is more severe in spam detection and impedes the process, is high-dimensionality of feature space. Various dimension reduction methods have been proposed that produce a lower dimensional space compared to the original. These methods are divided mainly into two groups: feature selection and feature extraction. This research deals with dimension reduction in the text classification task and especially performs experiments in the spam detection field. We employ Information Gain (IG) and Chi-square Statistic (CHI) as well-known feature selection methods. Also, we propose a new feature extraction method called Sprinkled Semantic Feature Space (SSFS). Furthermore, this paper presents a new hybrid method called IG_SSFS. In IG_SSFS, we combine the selection and extraction processes to reap the benefits from both. To evaluate the mentioned methods in the spam detection field, experiments are conducted on some well-known email datasets. According to the results, SSFS demonstrated superior effectiveness over the basic selection methods in terms of improving classifiers’ performance, and IG_SSFS further enhanced the performance despite consuming less processing time.
INTRODUCTION:In times of crisis, the timely transfer of the injured to medical facilities is one of the most important stages of relief and one of the most widely used methods to achieve the transfer point designing goal. The transfer point in literature is a place to collect and transfer the optimal demand for a particular service. For example, in times of natural disasters such as earthquakes, the injured (customers) are transferred by ambulance to the transfer points and then by helicopter to the hospital (facility). METHODS: In this study, two single-objective and double-objective complex integer number programming models were presented for the problem of locating transfer points and optimal allocation to facilities, taking into account the limitations in facility capacity and transfer points as well as assuming two types of normal and bad injuries. FINDINGS: In the single-objective model, the reduction in the time of sending the injured in the relief chain, and in the double objective model, in addition to the previous goal, the reduction of the fine for not sending the injured were examined. It is only possible to transfer each injured person to the hospital using the transfer points, and the treatment of the normally injured individuals is performed at the transfer points. The models were solved with two approaches, mild and severe. In order to show the efficiency of the proposed models, a case study was conducted in districts 10, 11, and 17 of Tehran metropolis, Iran. CONCLUSION: Setting up transfer points has a great impact on speeding up the process of providing services to the injured. Additionally, given the disproportionality of the number of injured with the capacity of hospitals in severe crises, it is necessary to anticipate transfer points to manage relief and respond to all injured.
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