BayesRandomForest: An R Implementation of Bayesian Random Forest for Regression Analysis of High-Dimensional Data

Olaniran, Oyebayo Ridwan; Abdullah, Mohd Asrul Affendi

doi:10.1007/978-981-13-7279-7_33

Cited by 8 publications

(10 citation statements)

References 8 publications

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“…The random forest (RF) and Naïve Bayes (NB) algorithms were selected based on our references [31,32]. The third algorithm was the Ensemble (of RF and NB), which was selected using majority voting [33,34]. The R code is as shown in Figure 9 below.…”

Section: Methodsmentioning

confidence: 99%

Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm

et al. 2019

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Ransomware is a relatively new type of intrusion attack, and is made with the objective of extorting a ransom from its victim. There are several types of ransomware attacks, but the present paper focuses only upon the crypto-ransomware, because it makes data unrecoverable once the victim’s files have been encrypted. Therefore, in this research, it was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage. Successful detection at this stage is crucial to enable the attack to be stopped from achieving its objective. Once the victim was aware of the presence of crypto-ransomware, valuable data and files can be backed up to another location, and then an attempt can be made to clean the ransomware with minimum risk. Therefore we proposed a pre-encryption detection algorithm (PEDA) that consisted of two phases. In, PEDA-Phase-I, a Windows application programming interface (API) generated by a suspicious program would be captured and analyzed using the learning algorithm (LA). The LA can determine whether the suspicious program was a crypto-ransomware or not, through API pattern recognition. This approach was used to ensure the most comprehensive detection of both known and unknown crypto-ransomware, but it may have a high false positive rate (FPR). If the prediction was a crypto-ransomware, PEDA would generate a signature of the suspicious program, and store it in the signature repository, which was in Phase-II. In PEDA-Phase-II, the signature repository allows the detection of crypto-ransomware at a much earlier stage, which was at the pre-execution stage through the signature matching method. This method can only detect known crypto-ransomware, and although very rigid, it was accurate and fast. The two phases in PEDA formed two layers of early detection for crypto-ransomware to ensure zero files lost to the user. However in this research, we focused upon Phase-I, which was the LA. Based on our results, the LA had the lowest FPR of 1.56% compared to Naive Bayes (NB), Random Forest (RF), Ensemble (NB and RF) and EldeRan (a machine learning approach to analyze and classify ransomware). Low FPR indicates that LA has a low probability of predicting goodware wrongly.

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Section: Methodsmentioning

confidence: 99%

Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm

et al. 2019

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“…The topics in the classes include sports, politics, religion etc., which is diverse enough. The Precision (P) was used as class-specific index while Recall (R) (also known as sensitivity) is the proportion of the total amount of relevant cases that were actually retrieved [23][24][25][26][27][28][29]. The F 1 is a measure of the accuracy of the test dataset and it is defined as:…”

Section: Performance Evaluation Using 20-newgroup Datasetmentioning

confidence: 99%

GPLDA: A Generalized Poisson Latent Dirichlet Topic Model

Bala¹,

Saringat²

2019

IJACSA

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The earliest modification of Latent Dirichlet Allocation (LDA) in terms of words or document attributes is by relaxing its exchangeability assumption via the Bag-of-word (BoW) matrix. Several authors have proposed many modifications of the original LDA by focusing on model that assumes the current topic depends on the words from previous topic. Most of the earlier work ignored the document length distribution since it is assumed that it will fizzle out at the modelling stage. Thus, in this paper, the Poisson document length distribution of LDA model is replaced with Generalized Poisson (GP) distribution which has the strength of capturing complex structures. The main strengths of GP are in capturing overdispersed (variance larger than mean) and under dispersed (variance smaller than mean) count data. The Poisson distribution used by LDA strongly relies on the assumption that the mean and variance of document lengths are equal. This assumption is often unrealistic with most real-life text data where the variance of document length may be greater than or less than their mean. Approximate estimate of the GPLDA model parameters was achieved using Newton-Raphson approximation technique of log-likelihood. Performance and comparative analysis of GPLDA with LDA using accuracy and F 1 showed improved results.

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“…In this case, the proposed weighted Gini index returns the unweighted Gini index so that the variable is dropped at the splitting stage. The idea behind this is to control the mixture behaviour of hypergeometric distribution [30]. The dominant category determines the estimates of categories probability.…”

Section: Empirical Bayesian Random Forest (Ebrf)mentioning

confidence: 99%

“…The above derivation implies that irrespective of , RF can only guarantee 63% of relevant subset present in the entire predictor space. Thus, for [30] dataset, RF subset size empirical estimate guarantees selection of about 62.8% of relevant variables. Similar, percentile values were observed for the other datasets.…”

Section: Performance Comparisonmentioning

confidence: 99%

Empirical Bayesian Binary Classification Forests Using Bootstrap Prior

Olaniran¹,

Abdullah²,

Pillay³

et al. 2018

IJET

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In this paper, we present a new method called Empirical Bayesian Random Forest (EBRF) for binary classification problem. The prior ingredient for the method was obtained using the bootstrap prior technique. EBRF addresses explicitly low accuracy problem in Random Forest (RF) classifier when the number of relevant input variables is relatively lower compared to the total number of input variables. The improvement was achieved by replacing the arbitrary subsample variable size with empirical Bayesian estimate. An illustration of the proposed, and existing methods was performed using five high-dimensional microarray datasets that emanated from colon, breast, lymphoma and Central Nervous System (CNS) cancer tumours. Results from the data analysis revealed that EBRF provides reasonably higher accuracy, sensitivity, specificity and Area Under Receiver Operating Characteristics Curve (AUC) than RF in most of the datasets used.

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BayesRandomForest: An R Implementation of Bayesian Random Forest for Regression Analysis of High-Dimensional Data

Cited by 8 publications

References 8 publications

Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm

Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm

GPLDA: A Generalized Poisson Latent Dirichlet Topic Model

Empirical Bayesian Binary Classification Forests Using Bootstrap Prior

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