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Journal articles on the topic 'Random forest classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Zhao, Zi Ming, Cui Hua Li, Hua Shi, and Quan Zou. "Material Classification Using Random Forest." Advanced Materials Research 301-303 (July 2011): 73–79. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.73.

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Random forest has demonstrated excellent performance to deal with many problems of computer vision, such as image classification and keypoint recognition. This paper proposes an approach to classify materials, which combines random forest with MR8 filter bank. Firstly, we employ MR8 filter bank to filter the texture image. These filter responses are taken as texture feature. Secondly, Random forest grows on sub-window patches which are randomly extracted from these filter responses, then we use this trained forest to classify a given image (under unknown viewpoint and illumination) into texture classes. We carry out experiments on Columbia-Utrecht database. The experimental results show that our method successfully solves plain texture classification problem with high computational efficiency.

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Hatwell, Julian, Mohamed Medhat Gaber, and R. Muhammad Atif Azad. "CHIRPS: Explaining random forest classification." Artificial Intelligence Review 53, no. 8 (June 4, 2020): 5747–88. http://dx.doi.org/10.1007/s10462-020-09833-6.

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Abstract Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification. This rule is returned alongside estimates of the rule’s precision and coverage on the training data along with counter-factual details. An experimental study involving nine data sets shows that classification rules returned by CHIRPS have a precision at least as high as the state of the art when evaluated on unseen data (0.91–0.99) and offer a much greater coverage (0.04–0.54). Furthermore, CHIRPS uniquely controls against under- and over-fitting solutions by maximising novel objective functions that are better suited to the local (per instance) explanation setting.

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Paul, Angshuman, Dipti Prasad Mukherjee, Prasun Das, Abhinandan Gangopadhyay, Appa Rao Chintha, and Saurabh Kundu. "Improved Random Forest for Classification." IEEE Transactions on Image Processing 27, no. 8 (August 2018): 4012–24. http://dx.doi.org/10.1109/tip.2018.2834830.

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4

Razooq, Mohammed M., and Md Jan Nordin. "Texture Classification Using Random Forest." Advanced Science Letters 20, no. 10 (October 1, 2014): 1918–21. http://dx.doi.org/10.1166/asl.2014.5649.

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5

K., Vengatesan. "A Random Forest-based Classification Method for Prediction of Car Price." International Journal of Psychosocial Rehabilitation 24, no. 3 (March 30, 2020): 2639–48. http://dx.doi.org/10.37200/ijpr/v24i3/pr2020298.

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Chow, Una Y. "Random forest classification of Gitksan stops." Journal of the Acoustical Society of America 148, no. 4 (October 2020): 2473. http://dx.doi.org/10.1121/1.5146850.

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7

Rukmawan, S. H., F. R. Aszhari, Z. Rustam, and J. Pandelaki. "Classification of Infarction using Random Forest." Journal of Physics: Conference Series 1752, no. 1 (February 1, 2021): 012044. http://dx.doi.org/10.1088/1742-6596/1752/1/012044.

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8

Li, Teng, Bingbing Ni, Xinyu Wu, Qingwei Gao, Qianmu Li, and Dong Sun. "On random hyper-class random forest for visual classification." Neurocomputing 172 (January 2016): 281–89. http://dx.doi.org/10.1016/j.neucom.2014.10.101.

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9

Szűcs, Gábor. "Random Response Forest for Privacy-Preserving Classification." Journal of Computational Engineering 2013 (November 14, 2013): 1–6. http://dx.doi.org/10.1155/2013/397096.

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The paper deals with classification in privacy-preserving data mining. An algorithm, the Random Response Forest, is introduced constructing many binary decision trees, as an extension of Random Forest for privacy-preserving problems. Random Response Forest uses the Random Response idea among the anonymization methods, which instead of generalization keeps the original data, but mixes them. An anonymity metric is defined for undistinguishability of two mixed sets of data. This metric, the binary anonymity, is investigated and taken into consideration for optimal coding of the binary variables. The accuracy of Random Response Forest is presented at the end of the paper.

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Vimal, C., and B. Sathish. "Random Forest Classifier Based ECG Arrhythmia Classification." International Journal of Healthcare Information Systems and Informatics 5, no. 2 (April 2010): 1–10. http://dx.doi.org/10.4018/jhisi.2010040101.

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Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results indicate that a prediction accuracy of more than 98% can be obtained using the proposed method. This system can be further improved and fine-tuned for practical applications.

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Bakmeedeniya, A. H. M. T. C. "Random Forest Approach for Sleep Stage Classification." International Journal of Scientific and Research Publications (IJSRP) 10, no. 05 (May 18, 2020): 768–72. http://dx.doi.org/10.29322/ijsrp.10.05.2020.p10189.

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12

DONG, Long-jun, Xi-bing LI, and Kang PENG. "Prediction of rockburst classification using Random Forest." Transactions of Nonferrous Metals Society of China 23, no. 2 (February 2013): 472–77. http://dx.doi.org/10.1016/s1003-6326(13)62487-5.

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13

Lin, Peng, and Lixin Yang. "URBAN CLASSIFICATION BASED ON RANDOM FOREST ALGORITHM." International Journal of Advanced Research 7, no. 11 (November 30, 2019): 844–49. http://dx.doi.org/10.21474/ijar01/10084.

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14

Upadhyay, Anand, Ratan Singh, and Omkar Dhonde. "Random forest based classification of seagrass habitat." Journal of Information and Optimization Sciences 41, no. 2 (February 17, 2020): 613–20. http://dx.doi.org/10.1080/02522667.2020.1753303.

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15

Pal, M. "Random forest classifier for remote sensing classification." International Journal of Remote Sensing 26, no. 1 (January 2005): 217–22. http://dx.doi.org/10.1080/01431160412331269698.

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16

Hu, Xinyi, and Yaqun Zhao. "Block Ciphers Classification Based on Random Forest." Journal of Physics: Conference Series 1168 (February 2019): 032015. http://dx.doi.org/10.1088/1742-6596/1168/3/032015.

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17

Hayes, Matthew M., Scott N. Miller, and Melanie A. Murphy. "High-resolution landcover classification using Random Forest." Remote Sensing Letters 5, no. 2 (January 31, 2014): 112–21. http://dx.doi.org/10.1080/2150704x.2014.882526.

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18

Zhang, Youqiang, Guo Cao, Xuesong Li, and Bisheng Wang. "Cascaded Random Forest for Hyperspectral Image Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 4 (April 2018): 1082–94. http://dx.doi.org/10.1109/jstars.2018.2809781.

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19

Chao, Huang, Ma Yue-hua, Zhao Hai-bin, and Lu Xiao-ping. "Spectral Classification of Asteroids by Random Forest." Chinese Astronomy and Astrophysics 41, no. 4 (October 2017): 549–57. http://dx.doi.org/10.1016/j.chinastron.2017.11.006.

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20

Khasanah, Nurul, Rachman Komarudin, Nurul Afni, Yana Iqbal Maulana, and Agus Salim. "Skin Cancer Classification Using Random Forest Algorithm." SISFOTENIKA 11, no. 2 (May 4, 2021): 137. http://dx.doi.org/10.30700/jst.v11i2.1122.

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21

Kozachok, A. V., A. A. Spirin, and O. M. Golembiovskaya. "Random forest based pseudorandom sequences classification algorithm." Proceedings of Tomsk State University of Control Systems and Radioelectronics 23, no. 3 (September 25, 2020): 55–60. http://dx.doi.org/10.21293/1818-0442-2020-23-3-55-60.

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Recently, the number of confidential data leaks caused by internal violators has increased. Since modern DLP-systems cannot detect and prevent information leakage channels in encrypted or compressed form, an algorithm was proposed to classify pseudo-random sequences formed by data encryption and compression algorithms. Algorithm for constructing a random forest was used. An array of the frequency of occurrence of binary subsequences of 9-bit length and statistical characteristics of the byte distribution of sequences was chosen as the feature space. The presented algorithm showed the accuracy of 0,99 for classification of pseudorandom sequences. The proposed algorithm will improve the existing DLP-systems by increasing the accuracy of classification of encrypted and compressed data.

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22

KRASNOSHLYK, Nataliia, and Oleksandr PISKUN. "IMPLEMENTATION AND RESEARCH OF THE RANDOM FOREST ALGORITHM TO SOLVE CLASSIFICATION PROBLEMS." CHERKASY UNIVERSITY BULLETIN: APPLIED MATHEMATICS. INFORMATICS, no. 1 (2021): 69–77. http://dx.doi.org/10.31651/2076-5886-2020-1-69-77.

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23

Zhang, Yanxia, Yongheng Zhao, and Hongwen Zheng. "Automated Classification of Quasars and Stars." Proceedings of the International Astronomical Union 5, S267 (August 2009): 147. http://dx.doi.org/10.1017/s1743921310006083.

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We investigate selection and weighting of features by applying a random forest algorithm to multiwavelength data. Then we employ a k-nearest neighbor method to distinguish quasars from stars. We then compare the performance of this approach based on all features, weighted features, and selected features. We find that the k-nearest neighbor approach combined with random forests effectively separates quasars from stars.

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24

Choi, Seok-Hwan, Jin-Myeong Shin, and Yoon-Ho Choi. "Dynamic Nonparametric Random Forest Using Covariance." Security and Communication Networks 2019 (March 27, 2019): 1–12. http://dx.doi.org/10.1155/2019/3984031.

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As the representative ensemble machine learning method, the Random Forest (RF) algorithm has widely been used in diverse applications on behalf of the fast learning speed and the high classification accuracy. Research on RF can be classified into two categories: (1) improving the classification accuracy and (2) decreasing the number of trees in a forest. However, most of papers related to the performance improvement of RF have focused on improving the classification accuracy. Only some papers have focused on reducing the number of trees in a forest. In this paper, we propose a new Covariance-Based Dynamic RF algorithm, called C-DRF. Compared to the previous works, while ensuring the good-enough classification accuracy, the proposed C-DRF algorithm reduces the number of trees. Specifically, by computing the covariance between the number of trees in a forest and F-measure at each iteration, the proposed algorithm determines whether to increase the number of trees composing a forest. To evaluate the performance of the proposed C-DRF algorithm, we compared the learning time, the test time, and the memory usage with the original RF algorithm under the different areas of datasets. Under the same or higher classification accuracy, it is shown that the proposed C-DRF algorithm improves the performance of the original RF algorithm by as much as 58.68% at learning time, 47.91% at test time, and 68.06% in memory usage on average. As a practical application area, we also show that the proposed C-DRF algorithm is more efficient than the state-of-the-art RF algorithms in Network Intrusion Detection (NID) area.

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25

Agjee, Na’eem Hoosen, Onisimo Mutanga, Kabir Peerbhay, and Riyad Ismail. "The Impact of Simulated Spectral Noise on Random Forest and Oblique Random Forest Classification Performance." Journal of Spectroscopy 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/8316918.

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Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable noisy conditions, the extent of this is poorly understood. This study aimed to evaluate the influence of simulated spectral noise (10%, 20%, and 30%) on random forest (RF) and oblique random forest (oRF) classification performance using two node-splitting models (ridge regression (RR) and support vector machines (SVM)) to discriminate healthy and low infested water hyacinth plants. Results from this study showed that RF was slightly influenced by simulated noise with classification accuracies decreasing for week one and week two with the addition of 30% noise. In comparison to RF, oRF-RR and oRF-SVM yielded higher test accuracies (oRF-RR: 5.36%–7.15%; oRF-SVM: 3.58%–5.36%) and test kappa coefficients (oRF-RR: 10.72%–14.29%; oRF-SVM: 7.15%–10.72%). Notably, oRF-RR test accuracies and kappa coefficients remained consistent irrespective of simulated noise level for week one and week two while similar results were achieved for week three using oRF-SVM. Overall, this study has demonstrated that oRF-RR can be regarded a robust classification algorithm that is not influenced by noisy spectral conditions.

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26

Waśniewski, Adam, Agata Hościło, Bogdan Zagajewski, and Dieudonné Moukétou-Tarazewicz. "Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon." Forests 11, no. 9 (August 28, 2020): 941. http://dx.doi.org/10.3390/f11090941.

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This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.

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27

A, Dr Akila, and Ms Padma R. "Breast Cancer Tumor Categorization using Logistic Regression, Decision Tree and Random Forest Classification Techniques." International Journal of Research in Arts and Science 5, Special Issue (August 30, 2019): 282–89. http://dx.doi.org/10.9756/bp2019.1002/27.

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28

Khan, Zardad, Asma Gul, Aris Perperoglou, Miftahuddin Miftahuddin, Osama Mahmoud, Werner Adler, and Berthold Lausen. "Ensemble of optimal trees, random forest and random projection ensemble classification." Advances in Data Analysis and Classification 14, no. 1 (June 12, 2019): 97–116. http://dx.doi.org/10.1007/s11634-019-00364-9.

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29

Zanoni, Massimiliano. "Classification of NPL with a Random Forest approach." Risk Management Magazine 1, no. 2020 (April 8, 2020): 38–49. http://dx.doi.org/10.47473/2020rmm0007.

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30

Razooq, Mohammed Majeed. "Atexture Classification Using Random Forest And Decision Tree." VAWKUM Transactions on Computer Sciences 7, no. 2 (September 8, 2015): 1. http://dx.doi.org/10.21015/vtcs.v7i2.337.

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31

Xue, Jijun. "Casing Damage Classification Method Using Random Forest Algorithms." Journal of Physics: Conference Series 1437 (January 2020): 012131. http://dx.doi.org/10.1088/1742-6596/1437/1/012131.

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32

Peng, Bozhen, Dingyi Sun, Qifei Cui, and Chun Yin Yip. "Meteor Shower Scale Prediction Using Random Forest Classification." Journal of Physics: Conference Series 1486 (April 2020): 052007. http://dx.doi.org/10.1088/1742-6596/1486/5/052007.

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33

Aszhari, F. R., Z. Rustam, F. Subroto, and A. S. Semendawai. "Classification of thalassemia data using random forest algorithm." Journal of Physics: Conference Series 1490 (March 2020): 012050. http://dx.doi.org/10.1088/1742-6596/1490/1/012050.

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34

Akar, Özlem, and Oğuz Güngör. "Classification of multispectral images using Random Forest algorithm." Journal of Geodesy and Geoinformation 1, no. 2 (2012): 105–12. http://dx.doi.org/10.9733/jgg.241212.1.

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35

Gao, Dan, Yan-Xia Zhang, and Yong-Heng Zhao. "Random forest algorithm for classification of multiwavelength data." Research in Astronomy and Astrophysics 9, no. 2 (February 2009): 220–26. http://dx.doi.org/10.1088/1674-4527/9/2/011.

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36

Saiprasad, Ganesh, Chein-I. Chang, Nabile Safdar, Naomi Saenz, and Eliot Siegel. "Adrenal Gland Abnormality Detection Using Random Forest Classification." Journal of Digital Imaging 26, no. 5 (January 24, 2013): 891–97. http://dx.doi.org/10.1007/s10278-012-9554-7.

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37

Razi, Alireza Pedram, Zahra Einalou, and Mohammad Manthouri. "Sleep Apnea Classification Using Random Forest via ECG." Sleep and Vigilance 5, no. 1 (April 10, 2021): 141–46. http://dx.doi.org/10.1007/s41782-021-00138-4.

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38

Basri, Ashjan, and Muhammad Arif. "Classification of Seizure Types Using Random Forest Classifier." Advances in Science and Technology Research Journal 15, no. 3 (September 1, 2021): 167–78. http://dx.doi.org/10.12913/22998624/140542.

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Mesiar, Radko, and Ayyub Sheikhi. "Nonlinear Random Forest Classification, a Copula-Based Approach." Applied Sciences 11, no. 15 (August 2, 2021): 7140. http://dx.doi.org/10.3390/app11157140.

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In this work, we use a copula-based approach to select the most important features for a random forest classification. Based on associated copulas between these features, we carry out this feature selection. We then embed the selected features to a random forest algorithm to classify a label-valued outcome. Our algorithm enables us to select the most relevant features when the features are not necessarily connected by a linear function; also, we can stop the classification when we reach the desired level of accuracy. We apply this method on a simulation study as well as a real dataset of COVID-19 and for a diabetes dataset.

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Sheluhin, Oleg, Anna Vanyushina, and Mariya Gabisova. "The Filtering of Unwanted Applications in Internet Traffic Using Random Forest Classification Algorithm." Voprosy kiberbezopasnosti, no. 2(26) (2019): 44–51. http://dx.doi.org/10.21681/2311-3456-2018-2-44-51.

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41

Rachidi, Youssef. "Random Forest for video Text Amazigh." E3S Web of Conferences 229 (2021): 01062. http://dx.doi.org/10.1051/e3sconf/202122901062.

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In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the convolutional neural network (CNN) and the Random Forest method. We carried out the experiments with a database containing 3300 samples collected from different writers. The experimental results show that our proposed OCR system is very efficient and provides good recognition accuracy rate of handwriting characters images acquired via Video camera phone.

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42

Syukron, Akhmad, and Agus Subekti. "Penerapan Metode Random Over-Under Sampling dan Random Forest Untuk Klasifikasi Penilaian Kredit." Jurnal Informatika 5, no. 2 (September 29, 2018): 175–85. http://dx.doi.org/10.31311/ji.v5i2.4158.

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AbstrakPenilaian kredit telah menjadi salah satu cara utama bagi sebuah lembaga keuangan untuk menilai resiko kredit, meningkatkan arus kas, mengurangi kemungkinan resiko dan membuat keputusan manajerial. Salah satu permasalahan yang dihadapai pada penilaian kredit yaitu adanya ketidakseimbangan distribusi dataset. Metode untuk mengatasi ketidakseimbangan kelas yaitu dengan metode resampling, seperti menggunakan Oversampling, undersampling dan hibrida yaitu dengan menggabungkan kedua pendekatan sampling. Metode yang diusulkan pada penelitian ini adalah penerapan metode Random Over-Under Sampling Random Forest untuk meningkatkan kinerja akurasi klasifikasi penilaian kredit pada dataset German Credit. Hasil pengujian menunjukan bahwa klasifikasi tanpa melalui proses resampling menghasilkan kinerja akurasi rata-rata 70 % pada semua classifier. Metode Random Forest memiliki nilai akurasi yang lebih baik dibandingkan dengan beberapa metode lainnya dengan nilai akurasi sebesar 0,76 atau 76%. Sedangkan klasifikasi dengan penerapan metode Random Over-under sampling Random Forest dapat meningkatkan kinerja akurasi sebesar 14,1% dengan nilai akurasi sebesar 0,901 atau 90,1 %. Hasil penelitian menunjukan bahwa penerapan resampling dengan metode Random Over-Under Sampling pada algoritma Random Forest dapat meningkatkan kinerja akurasi secara efektif pada klasifikasi tidak seimbang untuk penilaian kredit pada dataset German Credit. Kata kunci: Penilaian Kredit, Random Forest, Klasifikasi, ketidakseimbangan kelas, Random Over-Under Sampling AbstractCredit scoring has become one of the main ways for a financial institution to assess credit risk, improve cash flow, reduce the possibility of risk and make managerial decisions. One of the problems faced by credit scoring is the imbalance in the distribution of datasets. The method to overcome class imbalances is the resampling method, such as using Oversampling, undersampling and hybrids by combining both sampling approaches. The method proposed in this study is the application of the Random Over-Under Sampling Random Forest method to improve the accuracy of the credit scoring classification performance on German Credit dataset. The test results show that the classification without going through the resampling process results in an average accuracy performance of 70% for all classifiers. The Random Forest method has a better accuracy value compared to some other methods with an accuracy value of 0.76 or 76%. While classification by applying the Random Over-under sampling + Random Forest method can improve accuracy performance 14.1% with an accuracy value of 0.901 or 90.1%. The results showed that the application of resampling using Random Over-Under Sampling method in the Random Forest algorithm can improve accuracy performance effectively on an unbalanced classification for credit scoring on German Credit dataset. Keywords: Imbalance Class, Credit Scoring, Random Forest, Classification, Resampling

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Syukron, Akhmad, and Agus Subekti. "Penerapan Metode Random Over-Under Sampling dan Random Forest Untuk Klasifikasi Penilaian Kredit." Jurnal Informatika 5, no. 2 (September 29, 2018): 175–85. http://dx.doi.org/10.31294/ji.v5i2.4158.

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AbstrakPenilaian kredit telah menjadi salah satu cara utama bagi sebuah lembaga keuangan untuk menilai resiko kredit, meningkatkan arus kas, mengurangi kemungkinan resiko dan membuat keputusan manajerial. Salah satu permasalahan yang dihadapai pada penilaian kredit yaitu adanya ketidakseimbangan distribusi dataset. Metode untuk mengatasi ketidakseimbangan kelas yaitu dengan metode resampling, seperti menggunakan Oversampling, undersampling dan hibrida yaitu dengan menggabungkan kedua pendekatan sampling. Metode yang diusulkan pada penelitian ini adalah penerapan metode Random Over-Under Sampling Random Forest untuk meningkatkan kinerja akurasi klasifikasi penilaian kredit pada dataset German Credit. Hasil pengujian menunjukan bahwa klasifikasi tanpa melalui proses resampling menghasilkan kinerja akurasi rata-rata 70 % pada semua classifier. Metode Random Forest memiliki nilai akurasi yang lebih baik dibandingkan dengan beberapa metode lainnya dengan nilai akurasi sebesar 0,76 atau 76%. Sedangkan klasifikasi dengan penerapan metode Random Over-under sampling Random Forest dapat meningkatkan kinerja akurasi sebesar 14,1% dengan nilai akurasi sebesar 0,901 atau 90,1 %. Hasil penelitian menunjukan bahwa penerapan resampling dengan metode Random Over-Under Sampling pada algoritma Random Forest dapat meningkatkan kinerja akurasi secara efektif pada klasifikasi tidak seimbang untuk penilaian kredit pada dataset German Credit. Kata kunci: Penilaian Kredit, Random Forest, Klasifikasi, ketidakseimbangan kelas, Random Over-Under Sampling AbstractCredit scoring has become one of the main ways for a financial institution to assess credit risk, improve cash flow, reduce the possibility of risk and make managerial decisions. One of the problems faced by credit scoring is the imbalance in the distribution of datasets. The method to overcome class imbalances is the resampling method, such as using Oversampling, undersampling and hybrids by combining both sampling approaches. The method proposed in this study is the application of the Random Over-Under Sampling Random Forest method to improve the accuracy of the credit scoring classification performance on German Credit dataset. The test results show that the classification without going through the resampling process results in an average accuracy performance of 70% for all classifiers. The Random Forest method has a better accuracy value compared to some other methods with an accuracy value of 0.76 or 76%. While classification by applying the Random Over-under sampling + Random Forest method can improve accuracy performance 14.1% with an accuracy value of 0.901 or 90.1%. The results showed that the application of resampling using Random Over-Under Sampling method in the Random Forest algorithm can improve accuracy performance effectively on an unbalanced classification for credit scoring on German Credit dataset. Keywords: Imbalance Class, Credit Scoring, Random Forest, Classification, Resampling

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Duan, Xiaoyi, Dong Chen, Xiaohong Fan, Xiuying Li, Ding Ding, and You Li. "Research and Implementation on Power Analysis Attacks for Unbalanced Data." Security and Communication Networks 2020 (May 22, 2020): 1–10. http://dx.doi.org/10.1155/2020/5695943.

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In the power analysis attack, when the Hamming weight model is used to describe the power consumption of the chip operation data, the result of the random forest (RF) algorithm is not ideal, so a random forest classification method based on synthetic minority oversampling technique (SMOTE) is proposed. It compensates for the problem that the random forest algorithm is affected by the data imbalance and the classification accuracy of the minority classification is low, which improves the overall classification accuracy rate. The experimental results show that when the training set data is 800, the random forest algorithm predicts the correct rate of 84%, but the classification accuracy of the minority data is 0%, and the SMOTE-based random forest algorithm improves the prediction accuracy of the same set of test data by 91%. The classification accuracy rate of a few categories has increased from 0% to 100%.

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Spracklen, Benedict D., and Dominick V. Spracklen. "Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians." Forests 10, no. 2 (February 5, 2019): 127. http://dx.doi.org/10.3390/f10020127.

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Old-growth forests are an important, rare and endangered habitat in Europe. The ability to identify old-growth forests through remote sensing would be helpful for both conservation and forest management. We used data on beech, Norway spruce and mountain pine old-growth forests in the Ukrainian Carpathians to test whether Sentinel-2 satellite images could be used to correctly identify these forests. We used summer and autumn 2017 Sentinel-2 satellite images comprising 10 and 20 m resolution bands to create 6 vegetation indices and 9 textural features. We used a Random Forest classification model to discriminate between dominant tree species within old-growth forests and between old-growth and other forest types. Beech and Norway spruce were identified with an overall accuracy of around 90%, with a lower performance for mountain pine (70%) and mixed forest (40%). Old-growth forests were identified with an overall classification accuracy of 85%. Adding textural features, band standard deviations and elevation data improved accuracies by 3.3%, 2.1% and 1.8% respectively, while using combined summer and autumn images increased accuracy by 1.2%. We conclude that Random Forest classification combined with Sentinel-2 images can provide an effective option for identifying old-growth forests in Europe.

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Schonlau, Matthias, and Rosie Yuyan Zou. "The random forest algorithm for statistical learning." Stata Journal: Promoting communications on statistics and Stata 20, no. 1 (March 2020): 3–29. http://dx.doi.org/10.1177/1536867x20909688.

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Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest. We overview the random forest algorithm and illustrate its use with two examples: The first example is a classification problem that predicts whether a credit card holder will default on his or her debt. The second example is a regression problem that predicts the logscaled number of shares of online news articles. We conclude with a discussion that summarizes key points demonstrated in the examples.

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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.

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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.

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Parhusip, Hanna Arini, Bambang Susanto, Lilik Linawati, Suryasatriya Trihandaru, Yohanes Sardjono, and Adella Septiana Mugirahayu. "Classification Breast Cancer Revisited with Machine Learning." International Journal on Data Science 1, no. 1 (May 7, 2020): 42–50. http://dx.doi.org/10.18517/ijods.1.1.42-50.2020.

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The article presents the study of several machine learning algorithms that are used to study breast cancer data with 33 features from 569 samples. The purpose of this research is to investigate the best algorithm for classification of breast cancer. The data may have different scales with different large range one to the other features and hence the data are transformed before the data are classified. The used classification methods in machine learning are logistic regression, k-nearest neighbor, Naive bayes classifier, support vector machine, decision tree and random forest algorithm. The original data and the transformed data are classified with size of data test is 0.3. The SVM and Naive Bayes algorithms have no improvement of accuracy with random forest gives the best accuracy among all. Therefore the size of data test is reduced to 0.25 leading to improve all algorithms in transformed data classifications. However, random forest algorithm still gives the best accuracy.

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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.

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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.

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Xu, Ning, Jiangping Wang, Guojun Qi, Thomas Huang, and Weiyao Lin. "Ontological Random Forests for Image Classification." International Journal of Information Retrieval Research 5, no. 3 (July 2015): 61–74. http://dx.doi.org/10.4018/ijirr.2015070104.

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Previous image classification approaches mostly neglect semantics, which has two major limitations. First, categories are simply treated independently while in fact they have semantic overlaps. For example, “sedan” is a specific kind of “car”. Therefore, it's unreasonable to train a classifier to distinguish between “sedan” and “car”. Second, image feature representations used for classifying different categories are the same. However, the human perception system is believed to use different features for different objects. In this paper, we leverage semantic ontologies to solve the aforementioned problems. The authors propose an ontological random forest algorithm where the splitting of decision trees are determined by semantic relations among categories. Then hierarchical features are automatically learned by multiple-instance learning to capture visual dissimilarities at different concept levels. Their approach is tested on two image classification datasets. Experimental results demonstrate that their approach not only outperforms state-of-the-art results but also identifies semantic visual features.

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