Log in

Relevant bibliographies by topics / Random Forests / Journal articles

To see the other types of publications on this topic, follow the link: Random Forests.

Journal articles on the topic 'Random Forests'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Random Forests.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Bagui, Sikha, and Timothy Bennett. "Optimizing Random Forests: Spark Implementations of Random Genetic Forests." BOHR International Journal of Engineering 1, no. 1 (2022): 44–52. http://dx.doi.org/10.54646/bije.009.

Full text

Abstract:

The Random Forest (RF) algorithm, originally proposed by Breiman [7], is a widely used machine learning algorithm that gains its merit from its fast learning speed as well as high classification accuracy. However, despite its widespread use, the different mechanisms at work in Breiman’s RF are not yet fully understood, and there is still on-going research on several aspects of optimizing the RF algorithm, especially in the big data environment. To optimize the RF algorithm, this work builds new ensembles that optimize the random portions of the RF algorithm using genetic algorithms, yielding Random Genetic Forests (RGF), Negatively Correlated RGF (NC-RGF), and Preemptive RGF (PFS-RGF). These ensembles are compared with Breiman’s classic RF algorithm in Hadoop’s big data framework using Spark on a large, high-dimensional network intrusion dataset, UNSW-NB15.

APA, Harvard, Vancouver, ISO, and other styles

2

Bagui, Sikha, and Timothy Bennett. "Optimizing random forests: spark implementations of random genetic forests." BOHR International Journal of Engineering 1, no. 1 (2022): 42–51. http://dx.doi.org/10.54646/bije.2022.09.

Full text

Abstract:

The Random Forest (RF) algorithm, originally proposed by Breiman et al. (1), is a widely used machine learning algorithm that gains its merit from its fast learning speed as well as high classification accuracy. However, despiteits widespread use, the different mechanisms at work in Breiman’s RF are not yet fully understood, and there is stillon-going research on several aspects of optimizing the RF algorithm, especially in the big data environment. To optimize the RF algorithm, this work builds new ensembles that optimize the random portions of the RF algorithm using genetic algorithms, yielding Random Genetic Forests (RGF), Negatively Correlated RGF (NC-RGF), and Preemptive RGF (PFS-RGF). These ensembles are compared with Breiman’s classic RF algorithm in Hadoop’s big data framework using Spark on a large, high-dimensional network intrusion dataset, UNSW-NB15.

APA, Harvard, Vancouver, ISO, and other styles

3

Roy, Marie-Hélène, and Denis Larocque. "Prediction intervals with random forests." Statistical Methods in Medical Research 29, no. 1 (February 21, 2019): 205–29. http://dx.doi.org/10.1177/0962280219829885.

Full text

Abstract:

The classical and most commonly used approach to building prediction intervals is the parametric approach. However, its main drawback is that its validity and performance highly depend on the assumed functional link between the covariates and the response. This research investigates new methods that improve the performance of prediction intervals with random forests. Two aspects are explored: The method used to build the forest and the method used to build the prediction interval. Four methods to build the forest are investigated, three from the classification and regression tree (CART) paradigm and the transformation forest method. For CART forests, in addition to the default least-squares splitting rule, two alternative splitting criteria are investigated. We also present and evaluate the performance of five flexible methods for constructing prediction intervals. This yields 20 distinct method variations. To reliably attain the desired confidence level, we include a calibration procedure performed on the out-of-bag information provided by the forest. The 20 method variations are thoroughly investigated, and compared to five alternative methods through simulation studies and in real data settings. The results show that the proposed methods are very competitive. They outperform commonly used methods in both in simulation settings and with real data.

APA, Harvard, Vancouver, ISO, and other styles

4

Mantero, Alejandro, and Hemant Ishwaran. "Unsupervised random forests." Statistical Analysis and Data Mining: The ASA Data Science Journal 14, no. 2 (February 5, 2021): 144–67. http://dx.doi.org/10.1002/sam.11498.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Martin, James B., and Dominic Yeo. "Critical random forests." Latin American Journal of Probability and Mathematical Statistics 15, no. 2 (2018): 913. http://dx.doi.org/10.30757/alea.v15-35.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Dmitry Devyatkin, A., and G. Oleg Grigoriev. "Random Kernel Forests." IEEE Access 10 (2022): 77962–79. http://dx.doi.org/10.1109/access.2022.3193385.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Guelman, Leo, Montserrat Guillén, and Ana M. Pérez-Marín. "Uplift Random Forests." Cybernetics and Systems 46, no. 3-4 (April 3, 2015): 230–48. http://dx.doi.org/10.1080/01969722.2015.1012892.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Athey, Susan, Julie Tibshirani, and Stefan Wager. "Generalized random forests." Annals of Statistics 47, no. 2 (April 2019): 1148–78. http://dx.doi.org/10.1214/18-aos1709.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Bernard, Simon, Sébastien Adam, and Laurent Heutte. "Dynamic Random Forests." Pattern Recognition Letters 33, no. 12 (September 2012): 1580–86. http://dx.doi.org/10.1016/j.patrec.2012.04.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Taylor, Jeremy M. G. "Random Survival Forests." Journal of Thoracic Oncology 6, no. 12 (December 2011): 1974–75. http://dx.doi.org/10.1097/jto.0b013e318233d835.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Ishwaran, Hemant, Udaya B. Kogalur, Eugene H. Blackstone, and Michael S. Lauer. "Random survival forests." Annals of Applied Statistics 2, no. 3 (September 2008): 841–60. http://dx.doi.org/10.1214/08-aoas169.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Amaratunga, D., J. Cabrera, and Y. S. Lee. "Enriched random forests." Bioinformatics 24, no. 18 (July 22, 2008): 2010–14. http://dx.doi.org/10.1093/bioinformatics/btn356.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Biau, Gérard, Erwan Scornet, and Johannes Welbl. "Neural Random Forests." Sankhya A 81, no. 2 (June 21, 2018): 347–86. http://dx.doi.org/10.1007/s13171-018-0133-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Balińska, Krystyna T., Louis V. Quintas, and Jerzy Szymański. "Random recursive forests." Random Structures and Algorithms 5, no. 1 (January 1994): 3–12. http://dx.doi.org/10.1002/rsa.3240050103.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Pitman, Jim. "Coalescent Random Forests." Journal of Combinatorial Theory, Series A 85, no. 2 (February 1999): 165–93. http://dx.doi.org/10.1006/jcta.1998.2919.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Segal, Mark, and Yuanyuan Xiao. "Multivariate random forests." WIREs Data Mining and Knowledge Discovery 1, no. 1 (January 2011): 80–87. http://dx.doi.org/10.1002/widm.12.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Broutin, Nicolas, Luc Devroye, Gábor Lugosi, and Roberto Imbuzeiro Oliveira. "Subtractive random forests." Latin American Journal of Probability and Mathematical Statistics 21, no. 1 (2024): 575. http://dx.doi.org/10.30757/alea.v21-23.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Dai, Hongsheng. "Perfect sampling methods for random forests." Advances in Applied Probability 40, no. 3 (September 2008): 897–917. http://dx.doi.org/10.1239/aap/1222868191.

Full text

Abstract:

A weighted graphGis a pair (V, ℰ) containing vertex setVand edge set ℰ, where each edgee∈ ℰ is associated with a weightWe. A subgraph ofGis a forest if it has no cycles. All forests on the graphGform a probability space, where the probability of each forest is proportional to the product of the weights of its edges. This paper aims to simulate forests exactly from the target distribution. Methods based on coupling from the past (CFTP) and rejection sampling are presented. Comparisons of these methods are given theoretically and via simulation.

APA, Harvard, Vancouver, ISO, and other styles

19

Dai, Hongsheng. "Perfect sampling methods for random forests." Advances in Applied Probability 40, no. 03 (September 2008): 897–917. http://dx.doi.org/10.1017/s0001867800002846.

Full text

Abstract:

A weighted graph G is a pair (V, ℰ) containing vertex set V and edge set ℰ, where each edge e ∈ ℰ is associated with a weight We . A subgraph of G is a forest if it has no cycles. All forests on the graph G form a probability space, where the probability of each forest is proportional to the product of the weights of its edges. This paper aims to simulate forests exactly from the target distribution. Methods based on coupling from the past (CFTP) and rejection sampling are presented. Comparisons of these methods are given theoretically and via simulation.

APA, Harvard, Vancouver, ISO, and other styles

20

Yu, Tianyu, Cuiwei Liu, Zhuo Yan, and Xiangbin Shi. "A Multi-Task Framework for Action Prediction." Information 11, no. 3 (March 16, 2020): 158. http://dx.doi.org/10.3390/info11030158.

Full text

Abstract:

Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different characteristics at different temporal stages. To this end, we propose a novel multi-task deep forest framework, which treats temporal progress analysis as a relevant task to action prediction and takes advantage of observation ratio labels of incomplete videos during training. The proposed multi-task deep forest is a cascade structure of random forests and multi-task random forests. Unlike the traditional single-task random forests, multi-task random forests are built upon incomplete training videos annotated with action labels as well as temporal progress labels. Meanwhile, incorporating both random forests and multi-task random forests can increase the diversity of classifiers and improve the discriminative power of the multi-task deep forest. Experiments on the UT-Interaction and the BIT-Interaction datasets demonstrate the effectiveness of the proposed multi-task deep forest.

APA, Harvard, Vancouver, ISO, and other styles

21

Zhang, Gongqiao, and Gangying Hui. "Random Trees Are the Cornerstones of Natural Forests." Forests 12, no. 8 (August 6, 2021): 1046. http://dx.doi.org/10.3390/f12081046.

Full text

Abstract:

Natural forests serve as the main component of the forest ecosystem. An in-depth interpretation of tree composition and structure of forest community is of great significance for natural forest conservation, monitoring, management, and near-natural silviculture of plantation forest. In this study, we explored the importance of key tree groups—random trees—in natural communities, compared the similarity between the random trees and the communities. This research studies six stem-mapped permanent plots (100 × 100 m2) of the typical natural forests in three different geographic regions of China. Several variables and their distributions were applied to study community characteristics comprehensively, including species abundance, diameter distribution, spatial pattern, mingling, crowding, and competition. The genetic absolute distance method is used to analyze the similarity between the random trees and the communities. Our results show that the features of random trees are highly consistent with the communities. The study proposes that random trees are the cornerstones of natural forests. Its quantitative advantage explains the key role that random trees play in natural forests. The study could provide a scientific insight into the protection, monitoring, and management of forests.

APA, Harvard, Vancouver, ISO, and other styles

22

Łuczak, Tomasz, and Boris Pittel. "Components of Random Forests." Combinatorics, Probability and Computing 1, no. 1 (March 1992): 35–52. http://dx.doi.org/10.1017/s0963548300000067.

Full text

Abstract:

A forest ℱ(n, M) chosen uniformly from the family of all labelled unrooted forests with n vertices and M edges is studied. We show that, like the Érdős-Rényi random graph G(n, M), the random forest exhibits three modes of asymptotic behaviour: subcritical, nearcritical and supercritical, with the phase transition at the point M = n/2. For each of the phases, we determine the limit distribution of the size of the k-th largest component of ℱ(n, M). The similarity to the random graph is far from being complete. For instance, in the supercritical phase, the giant tree in ℱ(n, M) grows roughly two times slower than the largest component of G(n, M) and the second largest tree in ℱ(n, M) is of the order n⅔ for every M = n/2 +s, provided that s3n−2 → ∞ and s = o(n), while its counterpart in G(n, M) is of the order n2s−2 log(s3n−2) ≪ n⅔.

APA, Harvard, Vancouver, ISO, and other styles

23

Goehry, Benjamin. "Random forests for time-dependent processes." ESAIM: Probability and Statistics 24 (2020): 801–26. http://dx.doi.org/10.1051/ps/2020015.

Full text

Abstract:

Random forests were introduced by Breiman in 2001. We study theoretical aspects of both original Breiman’s random forests and a simplified version, the centred random forests. Under the independent and identically distributed hypothesis, Scornet, Biau and Vert proved the consistency of Breiman’s random forest, while Biau studied the simplified version and obtained a rate of convergence in the sparse case. However, the i.i.d hypothesis is generally not satisfied for example when dealing with time series. We extend the previous results to the case where observations are weakly dependent, more precisely when the sequences are stationary β−mixing.

APA, Harvard, Vancouver, ISO, and other styles

24

Sun, Jianyuan, Guoqiang Zhong, Kaizhu Huang, and Junyu Dong. "Banzhaf random forests: Cooperative game theory based random forests with consistency." Neural Networks 106 (October 2018): 20–29. http://dx.doi.org/10.1016/j.neunet.2018.06.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Kumano, So, and Tatsuya Akutsu. "Comparison of the Representational Power of Random Forests, Binary Decision Diagrams, and Neural Networks." Neural Computation 34, no. 4 (March 23, 2022): 1019–44. http://dx.doi.org/10.1162/neco_a_01486.

Full text

Abstract:

Abstract In this letter, we compare the representational power of random forests, binary decision diagrams (BDDs), and neural networks in terms of the number of nodes. We assume that an axis-aligned function on a single variable is assigned to each edge in random forests and BDDs, and the activation functions of neural networks are sigmoid, rectified linear unit, or similar functions. Based on existing studies, we show that for any random forest, there exists an equivalent depth-3 neural network with a linear number of nodes. We also show that for any BDD with balanced width, there exists an equivalent shallow depth neural network with a polynomial number of nodes. These results suggest that even shallow neural networks have the same or higher representation power than deep random forests and deep BDDs. We also show that in some cases, an exponential number of nodes are required to express a given random forest by a random forest with a much fewer number of trees, which suggests that many trees are required for random forests to represent some specific knowledge efficiently.

APA, Harvard, Vancouver, ISO, and other styles

26

Désir, Chesner, Simon Bernard, Caroline Petitjean, and Laurent Heutte. "One class random forests." Pattern Recognition 46, no. 12 (December 2013): 3490–506. http://dx.doi.org/10.1016/j.patcog.2013.05.022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Seyedhosseini, Mojtaba, and Tolga Tasdizen. "Disjunctive normal random forests." Pattern Recognition 48, no. 3 (March 2015): 976–83. http://dx.doi.org/10.1016/j.patcog.2014.08.023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Xu, Ruo, Dan Nettleton, and Daniel J. Nordman. "Case-Specific Random Forests." Journal of Computational and Graphical Statistics 25, no. 1 (January 2, 2016): 49–65. http://dx.doi.org/10.1080/10618600.2014.983641.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Karlsson, Isak, Panagiotis Papapetrou, and Henrik Boström. "Generalized random shapelet forests." Data Mining and Knowledge Discovery 30, no. 5 (July 12, 2016): 1053–85. http://dx.doi.org/10.1007/s10618-016-0473-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Scornet, Erwan, Gérard Biau, and Jean-Philippe Vert. "Consistency of random forests." Annals of Statistics 43, no. 4 (August 2015): 1716–41. http://dx.doi.org/10.1214/15-aos1321.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Jaeger, Byron C., D. Leann Long, Dustin M. Long, Mario Sims, Jeff M. Szychowski, Yuan-I. Min, Leslie A. Mcclure, George Howard, and Noah Simon. "Oblique random survival forests." Annals of Applied Statistics 13, no. 3 (September 2019): 1847–83. http://dx.doi.org/10.1214/19-aoas1261.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Puerta, Ronald, and José Iannacone. "Analysis of forest cover in Parque Nacional Tingo María (Peru) using the random forest algorithm." Scientia Agropecuaria 14, no. 3 (August 11, 2023): 291–300. http://dx.doi.org/10.17268/sci.agropecu.2023.025.

Full text

Abstract:

The establishment of natural protected areas is one of the most effective strategies to conserve forests and their biodiversity; however, the uncontrolled advance of deforestation resulting from the change of use to expand the agricultural frontier has become a threat to these intangible areas. This research aimed to analyze the dynamics of forest cover in Parque Nacional Tingo María (PNTM) and its buffer zone (ZA) located in the high jungle of the Huánuco region of Peru. The main input was Sentinel-2 images that were classified using the Random Forest algorithm. As a result, coverage maps were obtained for the study area corresponding to the years 2017, 2019, 2021 and 2023, achieving considerable thematic accuracy. During the evaluation periods, the rates of change from forest to non-forest within the PNTM presented low values -0.26% (2017 - 2019); -1.24% (2019 - 2021) and -0.02% (2021 - 2023). While the forests in the ZA have undergone a dynamic transition, with rates of change of -2.97%; -4.39% and -1.15% derived from land use change. The landscape metrics suggest that the forests of the PNTM are moderately fragmented, and the forests of the ZA are strongly fragmented, which leads to the conclusion that the protected natural area has fulfilled its objective of maintaining vegetation cover.

APA, Harvard, Vancouver, ISO, and other styles

33

Sadorsky, Perry. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers." Journal of Risk and Financial Management 14, no. 5 (April 29, 2021): 198. http://dx.doi.org/10.3390/jrfm14050198.

Full text

Abstract:

Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging, stochastic gradient boosting, and random forests produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Stochastic gradient boosting accuracy is a few percentage points less than that of random forests for forecast horizons over 10 days. For those looking to forecast the direction of gold and silver prices, tree bagging and random forests offer an attractive combination of accuracy and ease of estimation. For each of gold and silver, a portfolio based on the random forests price direction forecasts outperformed a buy and hold portfolio.

APA, Harvard, Vancouver, ISO, and other styles

34

Huang, Tianbao, Guanglong Ou, Yong Wu, Xiaoli Zhang, Zihao Liu, Hui Xu, Xiongwei Xu, Zhenghui Wang, and Can Xu. "Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data." Remote Sensing 15, no. 14 (July 14, 2023): 3550. http://dx.doi.org/10.3390/rs15143550.

Full text

Abstract:

It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha−1, respectively, and the R2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha−1, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R2 was 0.53 and the RMSE was 68.16 Mg ha−1. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R2 of 0.43 and RMSE of 45.09 Mg ha−1. (3) RRF was the best model for the four forest types according to the mean values, with R2 and RMSE of 0.503 and 52.335 Mg ha−1, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity.

APA, Harvard, Vancouver, ISO, and other styles

35

Kilpatrick, Alexander James, Aleksandra Ćwiek, and Shigeto Kawahara. "Random forests, sound symbolism and Pokémon evolution." PLOS ONE 18, no. 1 (January 4, 2023): e0279350. http://dx.doi.org/10.1371/journal.pone.0279350.

Full text

Abstract:

This study constructs machine learning algorithms that are trained to classify samples using sound symbolism, and then it reports on an experiment designed to measure their understanding against human participants. Random forests are trained using the names of Pokémon, which are fictional video game characters, and their evolutionary status. Pokémon undergo evolution when certain in-game conditions are met. Evolution changes the appearance, abilities, and names of Pokémon. In the first experiment, we train three random forests using the sounds that make up the names of Japanese, Chinese, and Korean Pokémon to classify Pokémon into pre-evolution and post-evolution categories. We then train a fourth random forest using the results of an elicitation experiment whereby Japanese participants named previously unseen Pokémon. In Experiment 2, we reproduce those random forests with name length as a feature and compare the performance of the random forests against humans in a classification experiment whereby Japanese participants classified the names elicited in Experiment 1 into pre-and post-evolution categories. Experiment 2 reveals an issue pertaining to overfitting in Experiment 1 which we resolve using a novel cross-validation method. The results show that the random forests are efficient learners of systematic sound-meaning correspondence patterns and can classify samples with greater accuracy than the human participants.

APA, Harvard, Vancouver, ISO, and other styles

36

RAGHUNANDA, SUCHETA. "Error Classification based on Multinomial NB and Random Forests." Journal of Research on the Lepidoptera 51, no. 2 (June 25, 2020): 687–95. http://dx.doi.org/10.36872/lepi/v51i2/301127.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Kulyukin, Vladimir, Nikhil Ganta, and Anastasiia Tkachenko. "On Image Classification in Video Analysis of Omnidirectional Apis Mellifera Traffic: Random Reinforced Forests vs. Shallow Convolutional Networks." Applied Sciences 11, no. 17 (September 2, 2021): 8141. http://dx.doi.org/10.3390/app11178141.

Full text

Abstract:

Omnidirectional honeybee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a beehive over a period of time. Automated video analysis of such traffic is critical for continuous colony health assessment. In our previous research, we proposed a two-tier algorithm to measure omnidirectional bee traffic in videos. Our algorithm combines motion detection with image classification: in tier 1, motion detection functions as class-agnostic object location to generate regions with possible objects; in tier 2, each region from tier 1 is classified by a class-specific classifier. In this article, we present an empirical and theoretical comparison of random reinforced forests and shallow convolutional networks as tier 2 classifiers. A random reinforced forest is a random forest trained on a dataset with reinforcement learning. We present several methods of training random reinforced forests and compare their performance with shallow convolutional networks on seven image datasets. We develop a theoretical framework to assess the complexity of image classification by a image classifier. We formulate and prove three theorems on finding optimal random reinforced forests. Our conclusion is that, despite their limitations, random reinforced forests are a reasonable alternative to convolutional networks when memory footprints and classification and energy efficiencies are important factors. We outline several ways in which the performance of random reinforced forests may be improved.

APA, Harvard, Vancouver, ISO, and other styles

38

Prinzie, Anita, and Dirk Van den Poel. "Random Forests for multiclass classification: Random MultiNomial Logit." Expert Systems with Applications 34, no. 3 (April 2008): 1721–32. http://dx.doi.org/10.1016/j.eswa.2007.01.029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Contat, Alice, Jean-François Delmas, Jean-Jil Duchamps, Igor Kortchemski, and Michel Nassif. "On random trees and forests." ESAIM: Proceedings and Surveys 74 (November 2023): 19–37. http://dx.doi.org/10.1051/proc/202374019.

Full text

Abstract:

The first talk at the session Random trees and random forests “Journée MAS” (27/08/2021) was presented by I. Kortchemski. After a general up-to-date introduction to local and scaling limits of Bienaymé trees (which are discrete branching trees), he presented new results on precise behavior of the largest out-degree of large branching trees when the offspring distribution μ is subcritical with μ(n) of order n−β for large n and β > 2 or critical with μ(n) of order n−2. In the next talk, M. Nassif gave asymptotics of additive functionals of large Bienayme trees in the global regime, which can be understood using scaling limits. Looking at Cayley trees with fixed size, A. Contat established a surprising identity for randomly built independent sets. Eventually J.-J. Duchamps presented some results on the distribution of the discrete Moran forest, a random graph arising in a classical population model at equilibrium.

APA, Harvard, Vancouver, ISO, and other styles

40

Zhong, Yuan, Hongyu Yang, Yanci Zhang, and Ping Li. "Online Rebuilding Regression Random Forests." Knowledge-Based Systems 221 (June 2021): 106960. http://dx.doi.org/10.1016/j.knosys.2021.106960.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Xu, Ruo, Dan Nettleton, and Daniel J. Nordman. "Predictor augmentation in random forests." Statistics and Its Interface 7, no. 2 (2014): 177–86. http://dx.doi.org/10.4310/sii.2014.v7.n2.a3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Abdulsalam, Hanady, David B. Skillicorn, and Patrick Martin. "Classification Using Streaming Random Forests." IEEE Transactions on Knowledge and Data Engineering 23, no. 1 (January 2011): 22–36. http://dx.doi.org/10.1109/tkde.2010.36.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Zhang, Heng-Ru, Fan Min, and Xu He. "Aggregated Recommendation through Random Forests." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/649596.

Full text

Abstract:

Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation. In this paper, we propose a random forest approach to create aggregated recommender systems. The approach is used to predict the rating of a group of users to a kind of items. In the preprocessing stage, we merge user, item, and rating information to construct an aggregated decision table, where rating information serves as the decision attribute. We also model the data conversion process corresponding to the new user, new item, and both new problems. In the training stage, a forest is built for the aggregated training set, where each leaf is assigned a distribution of discrete rating. In the testing stage, we present four predicting approaches to compute evaluation values based on the distribution of each tree. Experiments results on the well-known MovieLens dataset show that the aggregated approach maintains an acceptable level of accuracy.

APA, Harvard, Vancouver, ISO, and other styles

44

Scornet, Erwan. "Tuning parameters in random forests." ESAIM: Proceedings and Surveys 60 (2017): 144–62. http://dx.doi.org/10.1051/proc/201760144.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Scornet, Erwan. "Random Forests and Kernel Methods." IEEE Transactions on Information Theory 62, no. 3 (March 2016): 1485–500. http://dx.doi.org/10.1109/tit.2016.2514489.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Genuer, Robin, Jean-Michel Poggi, and Christine Tuleau-Malot. "Variable selection using random forests." Pattern Recognition Letters 31, no. 14 (October 2010): 2225–36. http://dx.doi.org/10.1016/j.patrec.2010.03.014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Ishwaran, Hemant, and Udaya B. Kogalur. "Consistency of random survival forests." Statistics & Probability Letters 80, no. 13-14 (July 2010): 1056–64. http://dx.doi.org/10.1016/j.spl.2010.02.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Avena, Luca, Fabienne Castell, Alexandre Gaudillière, and Clothilde Mélot. "Random Forests and Networks Analysis." Journal of Statistical Physics 173, no. 3-4 (August 2, 2018): 985–1027. http://dx.doi.org/10.1007/s10955-018-2124-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Liu, Yiyi, and Hongyu Zhao. "Variable importance-weighted random forests." Quantitative Biology 5, no. 4 (November 6, 2017): 338–51. http://dx.doi.org/10.1007/s40484-017-0121-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Jiang, Liangxiao. "Learning random forests for ranking." Frontiers of Computer Science in China 5, no. 1 (December 4, 2010): 79–86. http://dx.doi.org/10.1007/s11704-010-0388-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!