An improved random forest classifier for multi-class classification

Chaudhary, Archana; Kolhe, Savita; Kamal, Raj

doi:10.1016/j.inpa.2016.08.002

Cited by 141 publications

(59 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, there are other advantages of using RF i.e., it is non-parametric (independent of the probability distribution of the dataset), robust to noise and can handle large datasets [27]. Since there were more than two species of fungus, a multiclass RF [33] model was built for prediction of species.…”

Section: Methodsmentioning

confidence: 99%

funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model

Meher¹,

Sahu²,

Gahoi³

et al. 2019

BMC Genet

View full text Add to dashboard Cite

BackgroundIdentification of unknown fungal species aids to the conservation of fungal diversity. As many fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For fungal taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of fungal species is challenging due to high degree of variability among ITS regions within species.ResultsA Random Forest (RF)-based predictor was built for identification of unknown fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi.ConclusionsAn online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for fungal species identification. Besides, an R-package funbarRF (https://cran.r-project.org/web/packages/funbarRF/) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of fungal taxonomy assignments based on DNA barcode.

show abstract

Section: Methodsmentioning

confidence: 99%

funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model

Meher¹,

Sahu²,

Gahoi³

et al. 2019

BMC Genet

View full text Add to dashboard Cite

show abstract

“…Ten-fold cross validation with either an 80-20 or 70-30 split was used for training and testing. Both binary class and multi-class classification [39] were considered. A Multilayer Perceptron (MLP) [40] served as the only classifier.…”

Section: Basnet Et Al [15] (Towards Detecting and Classifying Networmentioning

confidence: 99%

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

2020

View full text Add to dashboard Cite

The exponential growth in computer networks and network applications worldwide has been matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 were created to train predictive models on network-based intrusion detection. These datasets are not meant to serve as repositories for signature-based detection systems, but rather to promote research on anomaly-based detection through various machine learning approaches. CSE-CIC-IDS2018 contains about 16,000,000 instances collected over the course of ten days. It is the most recent intrusion detection dataset that is big data, publicly available, and covers a wide range of attack types. This multi-class dataset has a class imbalance, with roughly 17% of the instances comprising attack (anomalous) traffic. Our survey work contributes several key findings. We determined that the best performance scores for each study, where available, were unexpectedly high overall, which may be due to overfitting. We also found that most of the works did not address class imbalance, the effects of which can bias results in a big data study. Lastly, we discovered that information on the data cleaning of CSE-CIC-IDS2018 was inadequate across the board, a finding that may indicate problems with reproducibility of experiments. In our survey, major research gaps have also been identified.

show abstract

“…Data is basic source of knowledge and in case of big data [16,17,18,19], it travels through four different phases in its life cycle as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Polymorphic SBD Preprocessor: A Preprocessing Approach for Social Big Data

Jadiya¹,

Chaudhary²,

Thakur³

2020

INDJCSE

Self Cite

View full text Add to dashboard Cite

In recent years, the social media has become a powerful tool for sharing people thoughts and feelings. As a result data is being generated, analyzed and used with a tremendous growth rate. The data generated by numerous updates, comments, news, opinions and product reviews in social websites is very useful for getting insights. As there are multiple sources, the size, speed and formats of the gathered data affects the overall quality of information. To achieve quality information, preprocessing step is very important and decides future roadmap for efficient big data analysis approach. In context to social big data we are addressing the preprocessing phase which includes cleaning of data, identifying noise, data normalization, data transformation, handling missing values and data integration. In this paper we have proposed a new approach polymorphic SBD (Social Big Data) preprocessor which provides efficient results with multiple social big data sets. Also available data preprocessing methods for big data are presented in this paper. After efficient and successful data preprocessing steps, the output data set will be efficient, well formed and suitable source for any big data analysis approach to be applied afterwards. The paper also presents an example case and evaluates min-max normalization, z-score normalization and data mapping for the case presented.

show abstract

An improved random forest classifier for multi-class classification

Cited by 141 publications

References 34 publications

funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model

funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

Polymorphic SBD Preprocessor: A Preprocessing Approach for Social Big Data

Contact Info

Product

Resources

About