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Energy Efficient Data Mining Scheme for High Dimensional Data

Mohammad Alwadi, Girija Chetty
2015 Procedia Computer Science  
Efficient machine learning and data mining techniques provide unprecedented opportunity to monitor and characterize physical environments, such as forest cover type, using low cost wireless sensor networks  ...  In this paper, we propose energy efficient big data mining scheme for forest cover type and gas drift classification.  ...  After applying Naive Bayes, Random forest, J48 (Decision Trees), Random tree and Random committee classification for each batch, the average of the 10 batches have been taken.  ... 
doi:10.1016/j.procs.2015.02.047 fatcat:l2f4at4n2nfq7glmsaoawoqhou

Active Learning Strategy for COVID-19 Annotated Dataset

Amril Nazir, Ricky Maulana Fajri
2021 IEEE Access  
In this paper, a novel discriminative batch-mode active learning (DS3) is proposed to allow faster and more effective COVID-19 data annotation.  ...  Finally, the results of significance testing verify the effectiveness of DS3 and its superiority over baseline active learning algorithms.  ...  We compare the DS3 algorithm with other state-of-the-art batch-mode active learning algorithms.  ... 
doi:10.1109/access.2021.3130383 fatcat:kocgzwc57neohojjrm4r75fk2e

Online random forests based on CorrFS and CorrBE

Osman Hassab Elgawi
2008 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  
To this end we induce random forests algorithm into online mode and estimate the importance of variables incrementally based on correlation ranking (CR).  ...  This paper aims to contribute to the merits of online ensemble learning for classification problems.  ...  counterpart batch mode ensemble learning algorithms.  ... 
doi:10.1109/cvprw.2008.4563065 dblp:conf/cvpr/Osman08 fatcat:7wmwcqhn7jfuzaf4jlx72t27iq

Improving fraud prediction with incremental data balancing technique for massive data streams [article]

Rafiq Ahmed Mohammed, Kok-Wai Wong, Mohd Fairuz Shiratuddin, Xuequn Wang
2019 arXiv   pre-print
We applied Random Forest classification algorithm which can deal with the massive data stream. We investigated the suitability of Racing Algorithm and Random Forest in the proposed framework.  ...  Applying new technique in the proposed framework on the European Credit Card dataset, provided better results than the Batch mode.  ...  In addition, for comparison, we conducted a separate study in a Batch mode without re-balancing strategy for Random Forest classification. 1) Automated balancing strategy -Batch mode In the Batch mode  ... 
arXiv:1903.00410v2 fatcat:ivixlffvj5fhnbupgmtlx6ubly

Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement

Ning Gao, Xingyuan Wang, Xiukun Wang
2019 Applied Sciences  
Our method consists of the following processes: Firstly, an input image is encoded as a multi-mode Local Binary Pattern (LBP) image to regress the face shape parameters.  ...  Secondly, the local multi-mode histogram of oriented gradient (HOG) features is applied to update each landmark position. Thirdly, the above two alignment shapes are weighted as the final result.  ...  X.W. helped to modify the early version of the paper. X.W. helped to modify the later version. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9050977 fatcat:yahink5v2zbrvlazrdlmt2rob4

Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors

D. Reker, P. Schneider, G. Schneider
2016 Chemical Science  
Active machine learning puts artificial intelligence in charge of a sequential, feedback-driven discovery process.  ...  For the estimation of the architectural changes induced by the data, we trained a total of 10 random forest models on each of the three data sets from active learning cycles 0, 1, 2, and 3.  ...  We performed a second active learning cycle with the updated random forest model and the same balanced selection function (Table S4 †).  ... 
doi:10.1039/c5sc04272k pmid:30155037 fatcat:pm7keyxrzfb7hhwjdrb2xzyiym

Active Learning for Classifying Template Matches in Historical Maps [chapter]

Benedikt Budig, Thomas C. van Dijk
2015 Lecture Notes in Computer Science  
For that, we combine template matching (to locate possible occurrences) with active learning (to efficiently determine a classification).  ...  We propose an active-learning approach to one of the practical problems in automatic metadata extraction from historical maps: locating occurrences of image elements such as text or place markers.  ...  We thank Hans-Günter Schmidt of the Würzburg University Library for providing real data and practical use cases.  ... 
doi:10.1007/978-3-319-24282-8_5 fatcat:ajcbwfrt5jgqvjtyt5zccjzdni

Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning

E E O Ishida, R Beck, S González-Gaitán, R S de Souza, A Krone-Martins, J W Barrett, N Kennamer, R Vilalta, J M Burgess, B Quint, A Z Vitorelli, A Mahabal (+1 others)
2018 Monthly notices of the Royal Astronomical Society  
As a proof of concept, we use the simulated data released after the Supernova Photometric Classification Challenge (SNPCC) and a random forest classifier.  ...  The framework falls under the umbrella of active learning (AL), a class of algorithms that aims to minimize labelling costs by identifying a few, carefully chosen, objects which have high potential in  ...  This project is financially supported by CNRS as part of its MOMENTUM programme over the 2018-2020 period.  ... 
doi:10.1093/mnras/sty3015 fatcat:rsp4chkanbajdli6sll73n3ygi

A hybrid architecture for function approximation

Hassab Elgawi
2008 2008 6th IEEE International Conference on Industrial Informatics  
This paper proposes a new approach to build a value function estimation based on a combination of temporaldifferent (TD) and on-line variant of Random Forest (RF).  ...  The results demonstrate that a hybrid function approximation (Random-TD) can significantly improve the performance of TD methods.  ...  Q-learning was done with the discounting factor set to 0.99. The learning in the on-line mode relieves function approximator of the memory storage required as in the batch mode.  ... 
doi:10.1109/indin.2008.4618267 fatcat:mevmnn5ktngkzo3m6c3riyx6qu

conformalClassification: A Conformal Prediction R Package for Classification [article]

Niharika Gauraha, Ola Spjuth
2018 arXiv   pre-print
The package conformalClassification is built upon the random forest method, where votes of the random forest for each class are considered as the conformity scores for each data point.  ...  Conformal Prediction (CP) is a framework that complements the predictions of machine learning algorithms with reliable measures of confidence.  ...  Harrison for comments and recommendations during the preparation of this manuscript and R package.  ... 
arXiv:1804.05494v1 fatcat:vxxdfomdurc4hbwlajdo4v4a5i

An Efficient and Robust System for Vertically Federated Random Forest [article]

Houpu Yao, Jiazhou Wang, Peng Dai, Liefeng Bo, Yanqing Chen
2022 arXiv   pre-print
In this paper, we present a fast, accurate, scalable and yet robust system for vertically federated random forest.  ...  However, the efficiency of existing vertically federated learning algorithms remains to be a big problem, especially when applied to large-scale real-world datasets.  ...  In this paper, we present a highly efficient and robust vertically federated random forest system.  ... 
arXiv:2201.10761v1 fatcat:fqk44il7ffcvrl6jwphayl7hga

ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine Learning

Andrea Esuli
2022 IEEE Access  
labeling in active learning processes, and always assume some degree of batch processing of data.  ...  Our efficient implementation of the unobtrusive machine learning model combines various machine learning methods and technologies, such as hash-based feature mapping, random indexing, online learning,  ...  Batch-mode active learning methods [21] , [22] let the human work on sets of documents at a time, before waiting for the retrain and the definition of a new batch.  ... 
doi:10.1109/access.2022.3184009 fatcat:ru63xa67l5chvk7fd6ct3vrcsm

Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice

Xide Xia, Pavlos Protopapas, Finale Doshi-Velez
2016 Proceedings of the 2016 SIAM International Conference on Data Mining  
To optimize this decision-making process, we present a batch, cost-sensitive, active learning approach that exploits structure in the unlabeled dataset, accounts for label uncertainty, and minimizes annotation  ...  We next introduce an uncertainty-reducing selection criterion that encourages the batch of selected instances to span multiple clusters, in addition to taking into account annotation cost.  ...  Active Learning Batch mode active learning selects a group of instances to label at each iteration.  ... 
doi:10.1137/1.9781611974348.54 dblp:conf/sdm/XiaPD16 fatcat:n5yigvn5prdktkj63ckw4sjoby

Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers [article]

Francesco Daghero, Alessio Burrello, Chen Xie, Luca Benini, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
2022 arXiv   pre-print
Random Forests (RFs) are widely used Machine Learning models in low-power embedded devices, due to their hardware friendly operation and high accuracy on practically relevant tasks.  ...  The accuracy of a RF often increases with the number of internal weak learners (decision trees), but at the cost of a proportional increase in inference latency and energy consumption.  ...  ADAPTIVE RANDOM FORESTS A. Motivation Typical RFs are composed of a large number of trees (N ), in the order of 10s or even 100s.  ... 
arXiv:2205.13838v1 fatcat:rdvpwtlwyrcwhigp64rjut7bv4

I-BiDaaS - D3.3: Batch Processing Analytics module implementation final report

Raül Sirvent
2020 Zenodo  
The advances of WP3 from M19 to M30 are described in this deliverable, thus with respect to the Batch Processing Analytics module.  ...  The module includes two main parts: a pool of Machine Learning algorithms, and the software stack that supports executing them or any other user analytics tasks.  ...  Forest represents an extension of basic Random Forest.  ... 
doi:10.5281/zenodo.4608345 fatcat:mwgdnqfec5b35gdjkigbgqvxei
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