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Improved boosting algorithms using confidence-rated predictions
1998
Proceedings of the eleventh annual conference on Computational learning theory - COLT' 98
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We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. ...
We give a specific method for assigning confidences to the predictions of decision trees, a method closely related to one used by Quinlan. ...
Thanks also to Peter Bartlett for showing us the bound on generalization error in Section 5 using pseudodimension, and to Roland Freund and Tommi Jaakkola for useful comments on numerical methods. ...
doi:10.1145/279943.279960
dblp:conf/colt/SchapireS98
fatcat:4jthcn64ejek3ayhal5k66nf7q
Confidence-based multiclass AdaBoost for physical activity monitoring
2013
Proceedings of the 17th annual international symposium on International symposium on wearable computers - ISWC '13
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Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. The proposed algorithm is a variant of the AdaBoost.M1 that incorporates well established ideas for confidence based boosting. ...
The method is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository and it is also evaluated on an activity recognition and an intensity ...
Furthermore, the new algorithm uses the information about how confident the weak learners are to predict the class of the instances. ...
doi:10.1145/2493988.2494325
dblp:conf/iswc/ReissSH13
fatcat:6gt26t3cebeolnmv57qsi2fypu
A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring
2014
Personal and Ubiquitous Computing
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Moreover, the confidence values are also used in the prediction part of the algorithm: the more confident the weak learner is in a new instance's prediction the more it counts in the output of the combined ...
Furthermore, the new algorithm uses the information about how confident the weak learners are to predict the class of the instances. ...
doi:10.1007/s00779-014-0816-x
fatcat:46wkudfhenchzpouimcfncwn3i
Boosting Trees for Anti-Spam Email Filtering
[article]
2001
arXiv
pre-print
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Several variants of the AdaBoost algorithm with confidence-rated predictions [Schapire & Singer, 99] have been applied, which differ in the complexity of the base learners considered. ...
Two main conclusions can be drawn from our experiments: a) The boosting-based methods clearly outperform the baseline learning algorithms (Naive Bayes and Induction of Decision Trees) on the PU1 corpus ...
In this paper, we show that the AdaBoost algorithm with confidence-rated predictions is a very well suited algorithm for addressing the spam filtering problem. ...
arXiv:cs/0109015v1
fatcat:jc3xww54irbptanskfjcn4t574
Improved prediction rule ensembling through model-based data generation
[article]
2021
arXiv
pre-print
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The results indicate that the use of surrogacymodels can substantially improve the sparsity of PRE, while retaining predictive accuracy, especiallythrough the use of a nested surrogacy approach. ...
This article examines the use of surrogate modelsto improve performance of PRE, wherein the Lasso regression is trained with the help of a massivedataset generated by the (boosted) decision tree ensemble ...
However, attempts to use class predictions instead of logit values did not lead to improvements in accuracy. ...
arXiv:2109.13672v1
fatcat:4buwsaj2a5azfcb7dsup7fwdyq
Classification Algorithm Accuracy Improvement for Student Graduation Prediction Using Ensemble Model
2020
International Journal of Information and Education Technology
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The accuracy rate of the student graduation using models combination boosted to 87.60%
E. ...
To improve accuracy rate of test sets instances of logistic regression, combinations of predictions of set of classifiers were tested. The experiments were done using WEKA using majority of votes. ...
doi:10.18178/ijiet.2020.10.10.1449
fatcat:dcptmdrpwncevj47kbf5zhxjbu
Application specific loss minimization using gradient boosting
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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We also show that this novel framework is useful in identifying regions of high word error rate (WER) and can provide up to 20% relative improvement depending on the chosen operating point. ...
We also extend the original gradient boosting algorithm with Newton-Raphson method to speed up learning. ...
Comparison of gradient boosting and other methods
Identifying High Error Rate Decodes Predicting WER of ASR hypotheses is useful for controlling the error rate. ...
doi:10.1109/icassp.2011.5947449
dblp:conf/icassp/ZhangSSR11
fatcat:thm5t4aodff27p542yijlth4mu
Boosting in Location Space
[article]
2013
arXiv
pre-print
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Location-based boosting differs from previous boosting algorithms because it optimizes a new spatial loss function to combine object detectors, each of which may have marginal performance, into a single ...
Here we introduce a new concept: location-based boosting. ...
Practical application of location-based boosting to detecting small objects Location-based boosting is an abstract algorithm; it creates an ensemble using a source of confidence-rated detectors. ...
arXiv:1309.1080v1
fatcat:sr3swpnchrffxgyyu77o4j6ch4
Collaborative filtering with collective training
2011
Proceedings of the fifth ACM conference on Recommender systems - RecSys '11
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Essentially, the collective training paradigm builds multiple different Collaborative Filtering (CF) models separately, and augments the training ratings of each CF model by using the partial predictions ...
One way to address this rating sparsity problem is to develop more effective methods for training rating prediction models. ...
The Tri-CF Algorithm In this subsection, we introduce Tri-CF algorithm, which is based on item-oriented KNN (iKNN), uKNN and SVD and boosts one CF model with the augmented ratings generated from the predictions ...
doi:10.1145/2043932.2043983
dblp:conf/recsys/GeXTL11
fatcat:naa4lexlovhpjbya7sbbb3slya
Active online confidence boosting for efficient object classification
2015
2015 IEEE International Conference on Robotics and Automation (ICRA)
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Our underlying classifier is from the family of boosting methods, but in contrast to earlier methods, our Confidence Boosting particularly focusses on misclassified samples that have a high classification ...
We present a novel e cient algorithm for object classification. ...
Extension to Confidence Boosting As can be seen from Eq. (5), the agreement a g used by standard gradient boost is only related to the prediction itself, but not to the confidence of the prediction. ...
doi:10.1109/icra.2015.7139368
dblp:conf/icra/MundTC15
fatcat:mwasjngtzfcsjjcsexowxn2mkq
Track Boosting and Synthetic Data Aided Drone Detection
[article]
2021
arXiv
pre-print
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Our method approaches the drone detection problem by fine-tuning a YOLOv5 model with real and synthetically generated data using a Kalman-based object tracker to boost detection confidence. ...
As the usage of drones increases with lowered costs and improved drone technology, drone detection emerges as a vital object detection task. ...
Object Tracking And Tracker Based Confidence Boosting Object tracking algorithms are used to provide continuity of object detections over time. ...
arXiv:2111.12389v2
fatcat:ji7ztulnlzby7ljxgrr3kdftfe
Pairwise Classification as an Ensemble Technique
[chapter]
2002
Lecture Notes in Computer Science
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The performance gain is not as large as for bagging and boosting, but on the other hand round robin ensembles have a clearly defined semantics. ...
In particular, we show that the use of round robin ensembles will also increase the classification performance of decision tree learners, even though they can directly handle multi-class problems. ...
confidence in its prediction. ...
doi:10.1007/3-540-36755-1_9
fatcat:ymtndsjwxfem3ecokkrvjcduru
Genetic boosting classification for malware detection
2016
2016 IEEE Congress on Evolutionary Computation (CEC)
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In the last few years, virus writers have made use of new obfuscation techniques with the ultimate aim of hindering malware from being detected and making the detection more complicated. ...
We have used this benchmark dataset as a starting point to improve these results using Genetic Boosting. ...
While existing evidence show the difficulty of achieve high accuracy rates on Malware classification, it is justified the use of more complex classification models, as a Boosting algorithm, to overcome ...
doi:10.1109/cec.2016.7743902
dblp:conf/cec/MartinMC16
fatcat:ndb44at4afbo7jpcswy55rwk7y
Robust Object Tracking based on Detection with Soft Decision
2008
2008 IEEE Workshop on Motion and video Computing
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Object trajectories are initialized from the responses of higher confidence; hypothesized objects are tracked by associating with all the responses in the order of their confidence levels. ...
Responses of different confidence levels are generated by classifiers with different complexities. ...
To improve the robustness of object tracking, we propose a method that makes "soft decisions" at the detection stage by producing detection responses of different confidence levels and uses these confidence ...
doi:10.1109/wmvc.2008.4544052
fatcat:4wuj63qjjfb67a7e52ehainwpm
Generating highly accurate prediction hypotheses through collaborative ensemble learning
2017
Scientific Reports
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Ensemble techniques 13-15 show improved accuracy of predictive analytics and data mining applications. ...
Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. ...
The algorithms were tested on various datasets, showing improved performance in both reducing the error rates and reducing the computation time. ...
doi:10.1038/srep44649
pmid:28304378
pmcid:PMC5356335
fatcat:kymz6d7fofcnnfs3gtjwxjse5u
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