New machine learning algorithm: random forest
14 Sep 2012-pp 246-252
TL;DR: This Paper gives an introduction of Random Forest, a new Machine Learning Algorithm and a new combination Algorithm that has been wildly used in classification and prediction, and used in regression too.
Abstract: This Paper gives an introduction of Random Forest. Random Forest is a new Machine Learning Algorithm and a new combination Algorithm. Random Forest is a combination of a series of tree structure classifiers. Random Forest has many good characters. Random Forest has been wildly used in classification and prediction, and used in regression too. Compared with the traditional algorithms Random Forest has many good virtues. Therefore the scope of application of Random Forest is very extensive.
Citations
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TL;DR: Well-known machine learning techniques, namely, SVM, random forest, and extreme learning machine (ELM) are applied and the results indicate that ELM outperforms other approaches in intrusion detection mechanisms.
Abstract: Intrusion detection is a fundamental part of security tools, such as adaptive security appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various intrusion detection techniques are used, but their performance is an issue. Intrusion detection performance depends on accuracy, which needs to improve to decrease false alarms and to increase the detection rate. To resolve concerns on performance, multilayer perceptron, support vector machine (SVM), and other techniques have been used in recent work. Such techniques indicate limitations and are not efficient for use in large data sets, such as system and network data. The intrusion detection system is used in analyzing huge traffic data; thus, an efficient classification technique is necessary to overcome the issue. This problem is considered in this paper. Well-known machine learning techniques, namely, SVM, random forest, and extreme learning machine (ELM) are applied. These techniques are well-known because of their capability in classification. The NSL–knowledge discovery and data mining data set is used, which is considered a benchmark in the evaluation of intrusion detection mechanisms. The results indicate that ELM outperforms other approaches.
379 citations
Cites background from "New machine learning algorithm: ran..."
...RF works by creating various decision trees in the training phase and output class labels those have the majority vote [12]....
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TL;DR: Analysis using a random forest method shows that the choices of emission inventory, grid resolution, and aerosol- and gas-phase chemistry are the top three factors affecting model performance for PM 2.5 and its chemical species in China.
Abstract: . Numerical air quality models (AQMs) have been applied more frequently over
the past decade to address diverse scientific and regulatory issues
associated with deteriorated air quality in China. Thorough evaluation of a
model's ability to replicate monitored conditions (i.e., a model performance
evaluation or MPE) helps to illuminate the robustness and reliability of the
baseline modeling results and subsequent analyses. However, with numerous
input data requirements, diverse model configurations, and the scientific
evolution of the models themselves, no two AQM applications are the same and
their performance results should be expected to differ. MPE procedures have
been developed for Europe and North America, but there is currently no
uniform set of MPE procedures and associated benchmarks for China. Here we
present an extensive review of model performance for fine particulate matter
(PM 2.5 ) AQM applications to China and, from this context, propose a set
of statistical benchmarks that can be used to objectively evaluate model
performance for PM 2.5 AQM applications in China. We compiled MPE
results from 307 peer-reviewed articles published between 2006 and 2019,
which applied five of the most frequently used AQMs in China. We analyze
influences on the range of reported statistics from different model
configurations, including modeling regions and seasons, spatial resolution
of modeling grids, temporal resolution of the MPE, etc. Analysis using a
random forest method shows that the choices of emission inventory, grid
resolution, and aerosol- and gas-phase chemistry are the top three factors
affecting model performance for PM 2.5 . We propose benchmarks for six
frequently used evaluation metrics for AQM applications in China, including
two tiers – “goals” and “criteria” – where goals represent the
best model performance that a model is currently expected to achieve and
criteria represent the model performance that the majority of studies
can meet. Our results formed a benchmark framework for the modeling
performance of PM 2.5 and its chemical species in China. For instance,
in order to meet the goal and criteria, the normalized mean bias (NMB) for
total PM 2.5 should be within 10 % and 20 %, while the normalized
mean error (NME) should be within 35 % and 45 %, respectively. The goal
and criteria values of correlation coefficients for evaluating hourly and
daily PM 2.5 are 0.70 and 0.60, respectively; corresponding values are
higher when the index of agreement (IOA) is used (0.80 for goal and 0.70 for
criteria). Results from this study will support the ever-growing modeling
community in China by providing a more objective assessment and context for
how well their results compare with previous studies and to better
demonstrate the credibility and robustness of their AQM applications prior
to subsequent regulatory assessments.
41 citations
Cites background or methods from "New machine learning algorithm: ran..."
...MNB) and MNE were only used in 15 and 10 studies, respectively, since as mentioned in Simon et al. (2012), these two metrics tend to give more weight to data at low values....
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...Random forest is a machine learning method suitable for classification and regression (Liu et al., 2012)....
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...Random Forest is a machine learning method suitable for classification and regression (Liu et al., 2012)....
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20 Mar 2021
TL;DR: The SVM algorithm is found to be the best algorithm that can be used to detect an intrusion in the system and gets a higher average precision compared with the other two algorithms.
Abstract: Intrusion detection systems (IDS) are used in analyzing huge data and diagnose anomaly traffic such as DDoS attack; thus, an efficient traffic classification method is necessary for the IDS. The IDS models attempt to decrease false alarm and increase true alarm rates in order to improve the performance accuracy of the system. To resolve this concern, three machine learning algorithms have been tested and evaluated in this research which are decision jungle (DJ), random forest (RF) and support vector machine (SVM). The main objective is to propose a ML-based network intrusion detection system (ML-based NIDS) model that compares the performance of the three algorithms based on their accuracy and precision of anomaly traffics. The knowledge discovery in databases (KDD) methodology and intrusion detection evaluation dataset (CIC-IDS2017) are used in the testing which both are considered as a benchmark in the evaluation of IDS. The average accuracy results of the SVM is 98.18%, RF is 96.76% and DJ is 96.50% in which the highest accuracy is achieved by the SVM. The average precision results of the SVM is 98.74, RF is 97.96 and DJ is 97.82 in which the SVM got a higher average precision compared with the other two algorithms. The average recall results of the SVM is 95.63, RF is 97.62 and DJ is 95.77 in which the RF achieves the highest average of recall than SVM and DJ. In overall, the SVM algorithm is found to be the best algorithm that can be used to detect an intrusion in the system.
33 citations
Cites result from "New machine learning algorithm: ran..."
...Irregular or random process of the RF has a low characteristic error, in contrast to some other classification algorithms [18]....
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TL;DR: This paper investigates the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images and investigates the application of normalization as a pre-processing step by two techniques.
Abstract: Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep learning models to automatically detect prostate cancer in whole-slide images. Specifically, we compare U-Net, DenseNet and EfficientNet. Models were developed on a multi-center cohort with 582 WSIs and subsequently evaluated on two independent test sets including 85 and 50 WSIs, respectively, to show the robustness of the proposed method to differing staining protocols and scanner types. We also investigated the application of normalization as a pre-processing step by two techniques, the whole-slide image color standardizer (WSICS) algorithm, and a cycle-GAN based method. For the two independent datasets we obtained an AUC of 0.92 and 0.83 respectively. After rescanning the AUC improves to 0.91/0.88 and after style normalization to 0.98/0.97. In the future our algorithm could be used to automatically pre-screen prostate biopsies to alleviate the workload of pathologists.
31 citations
TL;DR: This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring,Water- quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring.
Abstract: Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition to providing computer-assisted aid to complex issues surrounding water chemistry and physical/biological processes, artificial intelligence and machine-learning (AI/ML) applications are anticipated to further optimize water-based applications and decrease capital expenses. This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring, water-quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring. Although success in control, optimization, and modeling has been achieved with the AI methods, ML models, and smart technologies (including the Internet of Things (IoT), sensors, and systems based on these technologies) that are reviewed herein, key challenges and limitations were common and pervasive throughout. Poor data management, low explainability, poor model reproducibility and standardization, as well as a lack of academic transparency are all important hurdles to overcome in order to successfully implement these intelligent applications. Recommendations to aid explainability, data management, reproducibility, and model causality are offered in order to overcome these hurdles and continue the successful implementation of these powerful tools.
30 citations
References
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TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
"New machine learning algorithm: ran..." refers background in this paper
...s is the strength of the set of classifiers { ( , )} h θ x , ρ is the mean value of the correlation [6]....
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01 Aug 1996
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Abstract: Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.
16,118 citations
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Proceedings Article•
AT&T1
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
Abstract: In an earlier paper, we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that con- sistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a "pseudo-loss" which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's "bagging" method when used to aggregate various classifiers (including decision trees and single attribute- value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.
7,601 citations
TL;DR: A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
Abstract: Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy.
5,984 citations
"New machine learning algorithm: ran..." refers background in this paper
...Ho[4] has done much study on “the random subspace” method which grows each tree by a random selection of a subset of features....
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