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International Conference on Computing, Analytics and Networking (ICCAN)

作者： Arthi, J. Akoramurthy, B. Kongu Engn Coll Perundurai India IFET Coll Engn Villupuram India

ISBN: (纸本)9789811078712;9789811078705

The instigation of Non-Performing Assets (NPAs) in Indian banks was post 2009, when world was doing quantitative easing (QE) to save them off recession in 2008. The problem of NPA in India has witnessed the gross NPA rise to 79.7% and net NPA from 2.8% in September 2015 to 4.6% in March 2016. Banking has its NPA data increasing year by year which is a serious concern for the Indian banks. In this paper, an algorithm to determine and predict NPA is proposed. The proposed algorithm creates a pattern based on NPA data sets from various banks, and features are extracted to predict and eradicate financial debts. Also, a visual banking dashboard is developed exclusively for NPAs. The learning task in the proposed Feature based random forest algorithm is carried out by incorporating features extracted from the dataset considered and hence improves the prediction accuracy. The result of prediction is visualized using Tableau which, not only provides insights on the data but also aids in data-driven decision making.

关键词： NPA Data visualization random forest algorithm Prediction

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A Novel Learning Early-Warning Model Based on random forest algorithm 14th

A Novel Learning Early-Warning Model Based on Random Forest ...

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14th International Conference on Intelligent Tutoring Systems (ITS)

作者： Cheng, Xiaoxiao Zhu, Zhengzhou Liu, Xiao Yuan, Xiaofang Guo, Jiayu Guo, Qun Li, Deqi Zhu, Ruofei Peking Univ Beijing Peoples R China

ISBN: (纸本)9783319914640;9783319914633

The learning early-warning is an effective way to optimize the teaching effect and teach students in accordance of their aptitude. At present, the learning early-warning faces low accuracy, high value of MSE and MAE. We propose a novel learning early-warning model: LEWM-RFA. The model divides students' learning behaviors data into three dimensions: knowledge, behavior and attitude. Then the model uses random forest algorithm to extract features that can affect students' grades, and then predicts students' final exam scores. Students are divided into three warning levels according to their grades. Compared with the model based on the linear regression algorithm, the LEWM-RFA's MSE decreases by 27.498% and the LEWM-RFA's MAE decreases by 26.960%.

关键词： Learning early-warning random forest algorithm Prediction

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Detection of DNS DDoS Attacks with random forest algorithm on Spark 15

Detection of DNS DDoS Attacks with Random Forest Algorithm o...

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15th International Conference on Mobile Systems and Pervasive Computing (MobiSPC) / 13th International Conference on Future Networks and Communications (FNC)

作者： Chen, Liguo Zhang, Yuedong Zhao, Qi Geng, Guanggang Yan, ZhiWei Univ Chinese Acad Sci Sch Comp & Control Beijing 100190 Peoples R China China Comp Network Informat Ctr Beijing 100190 Peoples R China

Domain Name System(DNS) is one of the most foundational and essential services on the Internet, the security and robustness of DNS are of great significance. However, the stable operation of DNS has been threatened by Distributed Denial of Service(DDoS) for quite a long time, especially when the number of registered names of .CN are over 20 million on November 11, 2016. According to our observation, the frequency of volume-based DDoS attacks increased rapidly in recent years, and when the attack happened, not only the authoritative servers were affected, servers of Top Level Domain(TLD) also suffered a lot. In this paper, a model based on random forest([1]) is applied to traffic classification with an accuracy of 99.2% on Spark. The result shows that the model could be used to deal with large-scale DNS query flows, which is fast enough to be used in practice. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 13th International Conference on Future Networks and Communications, FNC-2018 and the 15th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2018.

关键词： DNS DDoS Traffic Filtering random forest algorithm Spark

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Research on the Classification of High Dimensional Imbalanced Data based on the Optimization of random forest algorithm 2018

Research on the Classification of High Dimensional Imbalance...

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International Conference on Big Data Engineering and Technology (BDET)

作者： Ma Xiaojuan Tianjin Transportat Vocat Coll Tianjin Peoples R China

ISBN: (纸本)9781450365826

The random forest is stochastic a forest establishment, there are many decision trees;there is no correlation between each decision tree random forest. The establishment of each decision tree, using the random sampling process is put back, and then uses the voting form of classification and prediction. The algorithm can solve the bottleneck in the performance of a single classifier, so it is widely used in many aspects. Of course, the algorithm also has some room for improvement, according to the random forest algorithm to deal with unbalanced data set when running low efficiency, this paper puts forward approaches to the problem are not a new balance at the same time as the calculation process, showing the growth of the index value, how to improve the prediction speed and shorten the running time, according to the characteristics of the random forest algorithm in the construction process is put forward Based on the domestic and foreign literatures, this paper mainly studies the optimization of random forest from two aspects. random forest algorithm is an ensemble learning method in the field of machine learning. It is integrated with the classification results of multiple decision trees to form a global classifier. The random forest algorithm compared with other classification algorithms have many advantages, the classified effect advantage is reflected in the classification accuracy and the generalization error is small and has the ability to deal with high dimensional data, the training process of the advantages of learning algorithm of quick and easy parallelization. Based on these two advantages, random forest algorithm has been widely used, and it has become one of the priorities to deal with classification problem. However, when the data type of the unbalanced distribution of the situation, that is the number one category of samples is far less than other types of samples, random forest algorithm will appear ineffective, the generalization error of variable c

关键词： Imbalanced data classification random forest algorithm optimization algorithm subspace selection model selection

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Vehicle Accident Foresight System using Bigdata Intelligent random forest algorithm 10

Vehicle Accident Foresight System using Bigdata Intelligent ...

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10th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Aburas, Abdurazzag A. Eyono, Scott Naidoo, Omesan Univ KwaZulu Natal Dept Comp Engn Durban South Africa

ISBN: (数字)9781728109749

ISBN: (纸本)9781728109749

commercial vehicles are used to improve people life. One of the main concern is the safety of these commercial vehicle during accidents. This research paper presents the idea of a Vehicle Accident Foresight System (VAFS). Accident datasets for VAFS are based on actual vehicle accident datasets extracted from open-source datasets. VAFS aims to analyze past accident data using the random forest algorithm to predict future accidents. One thousand decision trees are used in implementation. The implemented prototype consists of a dashboard which can specify accident locations according to time, it can also identify the elements which contribute the most to accident severity. The prediction model of VAFS has a minimum accuracy of 69% and a maximum accuracy of 100%.

关键词： Vehicle accident Precognition insurance datasets random forest algorithm

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Detection of DNS DDoS Attacks with random forest algorithm on Spark

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Procedia Computer Science 2018年 134卷 310-315页

作者： Liguo Chen Yuedong Zhang Qi Zhao Guanggang Geng ZhiWei Yan School of Computer and Control University of Chinese Academy of Sciences Zhongguancun Nansijie Haidian District Beijing 100190 China China Computer Network Information Center Zhongguancun Nansijie Haidian District Beijing 100190 China

关键词： DNS DDoS Traffic Filtering random forest algorithm Spark

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Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 143卷 59-65页

作者： Akkoc, Betul Arslan, Ahmet Kok, Hatice Selcuk Univ Fac Engn Dept Comp Engn Aleaddin Keykubad Campus TR-42075 Konya Turkey Konya Food & Agr Univ Dept Comp Engn Konya Turkey Necmettin Erbakan Univ Dept Orthodont Konya Turkey

Background and Objective: One of the first stages in the identification of an individual is gender determination. Through gender determination, the search spectrum can be reduced. In disasters such as accidents or fires, which can render identification somewhat difficult, durable teeth are an important source for identification. This study proposes a smart system that can automatically determine gender using 3D digital maxillary tooth plaster models. Methods: The study group was composed of 40 Turkish individuals (20 female, 20 male) between the ages of 21 and 24. Using the iterative closest point (ICP) algorithm, tooth models were aligned, and after the segmentation process, models were transformed into depth images. The local discrete cosine transform (DCT) was used in the process of feature extraction, and the random forest (RF) algorithm was used for the process of classification. Results: Classification was performed using 30 different seeds for random generator values and 10 fold cross-validation. A value of 85.166% was obtained for average classification accuracy (CA) and a value of 91.75% for the area under the ROC curve (AUC). Conclusions: A multi-disciplinary study is performed here that includes computer sciences, medicine and dentistry. A smart system is proposed for the determination of gender from 3D digital models of maxillary tooth plaster models. This study has the capacity to extend the field of gender determination from teeth. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Gender determination Feature extraction Image processing random forest algorithm

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Research on the Classification of High Dimensional Imbalanced Data Based on the Optimizational random forest algorithm 9

Research on the Classification of High Dimensional Imbalance...

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9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Bo, Su Guizhou Inst Technol Guiyang 550025 Guizhou Peoples R China

ISBN: (纸本)9781509048687

The random forest algorithm is a new classification and prediction model algorithm. So far, there is not much research on the problem of unbalanced data for random forest classification, ditto, no direct and effective method. On the basis of feature selection algorithm based on correlation measure, the integration feature selection method was helpful to increase the selection probability of classification feature of positive class samples, which used the subspace selection algorithm based on stratified sampling, sampling the feature subset generated by the integration feature selection method, respectively, which ensured the importance of the feature and the difference of the generated model. The simulation results show that the proposed method is effective.

关键词： random forest algorithm High dimension Unbalanced data Classification

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Covert Timing Channel Detection Method Based on random forest algorithm 17

Covert Timing Channel Detection Method Based on Random Fores...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Guangxin Fu Qingbao Li Ping Zhang Zhifeng Chen State Key laboratory of MEAC

ISBN: (纸本)9781509039456;9781509039449

Network stealth events emerging in endless stream, covert timing channel is one of the most difficult means to prevent. In order to further improve the detection rate of the covert timing channel under the condition of small embedded information length. In this paper, the detection method based on SVM is analyzed. On the basis of the above analysis, adds a variety of statistical features, and a detection method based on random forest algorithm is proposed. The Inter-Packet Delay sequence of the covert timing channel is described from the statistical features of each order, and the above characteristics are used as the communication fingerprint of the covert channel. Then, the classifier based on the random forest algorithm is trained according to the communication fingerprint of the sample, and the classifier is used to judge whether the channel to be detected is the normal channel. The experimental results show that the method can effectively detect the covert timing channel in the case where the length of the embedded information is small. Compared with existing related works, this method has a certain rate of improvement, and the importance of the proposed statistical features are evaluated.

关键词： coverttiming channel statistical features communication fingerprint random forest algorithm

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ENSEMBLE CLASSIFIER WITH random forest algorithm TO DEAL WITH IMBALANCED HEALTHCARE DATA

ENSEMBLE CLASSIFIER WITH RANDOM FOREST ALGORITHM TO DEAL WIT...

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Anbarasi, M. S. Janani, V. Pondicherry Engn Coll Dept Informat & Technol Pondicherry 605014 India

ISBN: (纸本)9781509061358

In day today life, data is generated in massive amount with rapid growth in health care environment. The medical industries have large amount of data sets, for diagnosis purpose and maintain patient's records. The medical researches come with new treatments and medicine every day. But availability of medical datasets is often not balanced in their class labels. The performance of some existing method is poor on imbalanced dataset. So the prediction of disease from imbalanced data becomes difficult to handle. In this proposal Classifier ensemble method (random forest algorithm) can be used to overcome existing classifier techniques. Multiple classifier system is more accurate and robust than an existing classifier technique. The ensemble method proves to be very efficient in classification of records from available imbalanced healthcare patient data, as it involves the process of considering opinion from multiple base classifiers, as opposed to the single classifier method. This method gives a very accurate and precise inference, as unrelated data's are removed because of multiple base classifiers. The problems of healthcare dataset especially with some uncertainty can be predicted.

关键词： Ensemble Classifier random forest algorithm Data pre-processing Anomaly Detection Technique Clustering technique

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