Integrating Genetic Algorithm with Random Forest for Improving the Classification Performance of Web Log Data

Mittal, Ruchi; Malik, Varun; Singh, Vikram; Singh, Jaiteg; Kaur, Amandeep

doi:10.1109/pdgc50313.2020.9315807

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Random Forest & Firefly algorithm (Farrell et al, 2020;Aslam et al, 2021) GA (Mittal et al, 2020) Kangaroo Mob optimization (Moldovan et al, 2019) PSO (Asadi et al, 2021;Zhou et al, 2020) Table 1 (continued)…”

Section: Convergence Velocitymentioning

confidence: 99%

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.

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Section: Convergence Velocitymentioning

confidence: 99%

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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“…The PRFITN algorithm mainly includes three stages: data dimensionality reduction, feature grouping, and parallel construction of random forests. (1) In the data dimension reduction stage, the DRIGFN strategy is proposed to accurately identify and delete redundant and irrelevant features in the dataset, and the dimensionality-reduced dataset DB * is obtained.…”

Section: Prfitn Algorithmmentioning

confidence: 99%

“…A classification algorithm is a supervised learning algorithm, which can discover classification rules and construct classification models based on labeled information, to predict the attributes of unlabeled data [1]. Among the classification algorithms, random forest (RF) has been used in text classification [2] and environmental prediction in recent years because of its strong stability and good tolerance to noise and outliers [3,4].…”

Section: Introductionmentioning

confidence: 99%

Decision Key-Value Feature Construction for Multihoming Big Data Network

Zahra¹,

Nagabushanam

Kumaraswamy³

et al. 2023

Wireless Communications and Mobile Computing

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The random forest algorithm under the MapReduce framework has too many redundant and irrelevant features, low training feature information, and low parallelization efficiency when dealing with multihoming big data network problems, so parallelism is based on information theory, and norms is proposed for random forest algorithm (PRFITN). In this paper, the technique used first builds a hybrid dimensional reduction approach (DRIGFN) focused on information gain and the Frobenius norm, successfully reducing the number of redundant and irrelevant features; then, an information theory feature is offered. This results in the dimensionality-reduced dataset. Finally, a technique is suggested in the Reduce stage. The features are grouped in the FGSIT strategy, and the stratified sampling approach is employed to assure the information quantity of the training features in the building of the decision tree in the random forest. When datasets are provided as key/value pairs, it is common to want to aggregate statistics across all objects with the same key. To acquire global classification results and achieve a rapid and equal distribution of key-value pairs, a key-value pair redistribution method (RSKP) is used, which improves the cluster’s parallel efficiency. The approach provides a superior classification impact in multihoming large data networks, particularly for datasets with numerous characteristics, according to the experimental findings. We can utilize feature selection and feature extraction together. In addition to minimizing overfitting and redundancy, lowering dimensionality contributes to improved human interpretation and cheaper computing costs through model simplicity.

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“…Random Forest in Figure 1 is a machine learning algorithm with an ensemble method that can be used for classification and regression [9][10][11][12]. A Random Forest consists of a collection of decision trees associated with a bootstrap sample from a dataset [4].…”

Section: Random Forestmentioning

confidence: 99%

Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm

Akbar,

Hariyadi,

Hanani

2023

IOTA

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Bruteforce is a hacking technique that launches an attack by guessing the username and password of the system that is the target of the attack. The Bruteforce attack on the MQTT protocol is an attack that often occurs on the IoT, so it is necessary to detect attacks on the MQTT protocol to find out normal traffic and brute force traffic. Random Forest was chosen because this method can classify a lot of data in a relatively short time, and the results from Random Forest can improve accuracy and prevent overfitting in the data classification process. This study uses two types of data: primary data from the hacking environment lab and secondary data from the IEEE Data Port MQTT-IOT-IDS2020 dataset. Trials on primary data and the results obtained are accuracy of 99.55%, precision of 100%, recall of 99.54%, and f-measure of 99.77%, the duration needed to get these results with 1796 data lines, i.e., for 0 seconds. As for the secondary data, the researcher obtained an accuracy of 99.77%, a precision of 100%, a recall of 99.43%, and an f-measure of 98.71%, the duration required to obtain these results with 85002 data lines, i.e., for 62 seconds.

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Integrating Genetic Algorithm with Random Forest for Improving the Classification Performance of Web Log Data

Cited by 8 publications

References 11 publications

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Decision Key-Value Feature Construction for Multihoming Big Data Network

Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm

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