Mutual Information-based Feature Selection Approach to Reduce High Dimension of Big Data

Win, Thee Zin; Kham, Nang Saing Moon

doi:10.1145/3278312.3278316

Cited by 10 publications

(12 citation statements)

References 11 publications

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“…As a rule of thumb, more features on hand provide more information and hence should provide better classification accuracy [ 10 ]. However, in many instances, if we have more number of features in a dataset, and we use this data to train a classification model, the model gets confused while learning all the features on data, which results in decreasing classification accuracy instead of increasing it [ 10 ] [ 11 ]. To add further, the computations required by any classification system for high-dimensional data is a very expensive task in terms of time and memory [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, in many instances, if we have more number of features in a dataset, and we use this data to train a classification model, the model gets confused while learning all the features on data, which results in decreasing classification accuracy instead of increasing it [ 10 ] [ 11 ]. To add further, the computations required by any classification system for high-dimensional data is a very expensive task in terms of time and memory [ 11 ]. Therefore, a technique called feature selection is leveraged in order to select relevant features from the available set of features in high-dimensional datasets [ 11 ] [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…To add further, the computations required by any classification system for high-dimensional data is a very expensive task in terms of time and memory [ 11 ]. Therefore, a technique called feature selection is leveraged in order to select relevant features from the available set of features in high-dimensional datasets [ 11 ] [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approaches for Developing Land Cover Mapping

Alzahrani

Kanan

2022

Applied Bionics and Biomechanics

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In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting features from high-resolution satellite images to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. Neural networks (NNs) and random forest (RF) classifiers were used to evaluate the proposed approach on a recent urban land cover dataset of nine different classes. Experimental results show that the proposed approach achieved better performance with RF classifier using only 27% of the features. The random forest tree has achieved highest accuracy 84.27%; it is concluded that the RF algorithm is an appropriate algorithm for classifying cover land.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approaches for Developing Land Cover Mapping

Alzahrani

Kanan

2022

Applied Bionics and Biomechanics

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“…Information Gain (IG): IG is one of the FS methods used in text classification, which utilizes a global filter-based approach [37]. IG is a method for evaluating entropybased features [38] and is widely used in statistics and machine learning [21]. The higher the entropy is, the more information about the feature is obtained [37].…”

Section: -Proposed Methodsmentioning

confidence: 99%

An Effective Method of Feature Selection in Persian Text for Improving the Accuracy of Detecting Request in Persian Messages on Telegram

zadeh¹,

Chahooki²

2021

jist

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In recent years, data received from social media has increased exponentially. They have become valuable sources of information for many analysts and businesses to expand their business. Automatic document classification is an essential step in extracting knowledge from these sources of information. In automatic text classification, words are assessed as a set of features. Selecting useful features from each text reduces the size of the feature vector and improves classification performance. Many algorithms have been applied for the automatic classification of text. Although all the methods proposed for other languages are applicable and comparable, studies on classification and feature selection in the Persian text have not been sufficiently carried out. The present research is conducted in Persian, and the introduction of a Persian dataset is a part of its innovation. In the present article, an innovative approach is presented to improve the performance of Persian text classification. The authors extracted 85,000 Persian messages from the Idekav-system, which is a Telegram search engine. The new idea presented in this paper to process and classify this textual data is on the basis of the feature vector expansion by adding some selective features using the most extensively used feature selection methods based on Local and Global filters. The new feature vector is then filtered by applying the secondary feature selection. The secondary feature selection phase selects more appropriate features among those added from the first step to enhance the effect of applying wrapper methods on classification performance. In the third step, the combined filter-based methods and the combination of the results of different learning algorithms have been used to achieve higher accuracy. At the end of the three selection stages, a method was proposed that increased accuracy up to 0.945 and reduced training time and calculations in the Persian dataset.

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“…For instance, it is used in IDSs. There are three techniques for selecting features [5][6][7]: wrapper [8], filter [9] and embedded techniques [10]. Through the embedded technique, the feature selection for a given learning algorithm is integrated into the training process.…”

Section: Introductionmentioning

confidence: 99%

A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Almomani

2020

Symmetry

193

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The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.

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Mutual Information-based Feature Selection Approach to Reduce High Dimension of Big Data

Cited by 10 publications

References 11 publications

Machine Learning Approaches for Developing Land Cover Mapping

Machine Learning Approaches for Developing Land Cover Mapping

An Effective Method of Feature Selection in Persian Text for Improving the Accuracy of Detecting Request in Persian Messages on Telegram

A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

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