A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News

Fernandes, Kelwin; Vinagre, Pedro Almeida; Cortez, Paulo

doi:10.1007/978-3-319-23485-4_53

Cited by 150 publications

(89 citation statements)

References 13 publications

(16 reference statements)

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“…The black dashed lines correspond to the medians of distributions, the vertical blue line corresponds to the 0.99 percentile of the MC standard deviation distribution, while the horizontal blue line shows the median percentile of the absolute error distribution of corresponding samples, which is equal to 0.783 in this case. Five-layer neural network with a 256-128-64 structure was used on the Online News Popularity dataset[26]. The Pearson correlation coefficient equals to 0.056, thus showing no linear relation between the absolute error and the MC standard deviation.…”

mentioning

confidence: 99%

Dropout-Based Active Learning for Regression

Tsymbalov

Panov

Shapeev

2018

Lecture Notes in Computer Science

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Active learning is relevant and challenging for high-dimensional regression models when the annotation of the samples is expensive. Yet most of the existing sampling methods cannot be applied to large-scale problems, consuming too much time for data processing. In this paper, we propose a fast active learning algorithm for regression, tailored for neural network models. It is based on uncertainty estimation from stochastic dropout output of the network. Experiments on both synthetic and realworld datasets show comparable or better performance (depending on the accuracy metric) as compared to the baselines. This approach can be generalized to other deep learning architectures. It can be used to systematically improve a machine-learning model as it offers a computationally efficient way of sampling additional data.

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confidence: 99%

Dropout-Based Active Learning for Regression

Tsymbalov

Panov

Shapeev

2018

Lecture Notes in Computer Science

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“…Year Relative location of CT slices [Graf et al, 2011] 53500 386 Relative location Online News Popularity [Fernandes et al, 2015] 39797 61 Number of shares KEGG Network [Shannon et al, 2003] 53414 24 Clustering coefficient for considered problems after the 16th iteration of the active learning procedure are presented in Figure 5. We see that the NNGP procedure is superior in terms of RMSE compared to MCDUE and random sampling.…”

Section: Experiments On Uci Datasetsmentioning

confidence: 99%

“…4 Related work 4.1 Active learning Active learning [Settles, 2012] (also known as adaptive design of experiments [Forrester et al, 2008] in statistics and engineering design) is a framework which allows the additional data points to be annotated by computing target function value and then added to the training set. The particular points to sample are usually chosen as the ones that maximize so-called acquisition function.…”

Section: Hydraulic Simulatormentioning

confidence: 99%

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

Tsymbalov

Makarychev

Shapeev

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation. Such an approach requires either some heuristics to sample a batch of design points at one active learning iteration, or retraining the neural network after adding each data point, which is computationally inefficient. Moreover, uncertainty estimates for neural networks sometimes are overconfident for the points lying far from the training sample. In this work we propose to approximate Bayesian neural networks (BNN) by Gaussian processes, which allows us to update the uncertainty estimates of predictions efficiently without retraining the neural network, while avoiding overconfident uncertainty prediction for out-of-sample points. In a series of experiments on real-world data including largescale problems of chemical and physical modeling, we show superiority of the proposed approach over the state-of-the-art methods.In the next section, we discuss the problem statement and the proposed approach in detail.

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“…Learning Repository The benchmark datasets used include housing [17], wine [18], Parkinson [19], online news [20], and concrete strength [21] (see Table 2). …”

Section: Benchmark Data From Uci Machinementioning

confidence: 99%

Hash-Based Feature Learning for Incomplete Continuous-Valued Data

Yuan

Tan²,

Cheruvelil

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Hash-based feature learning is a widely-used data mining approach for dimensionality reduction and for building linear models that are comparable in performance to their nonlinear counterpart. Unfortunately, such an approach is inapplicable to many real-world data sets because they are often riddled with missing values. Substantial data preprocessing is therefore needed to impute the missing values before the hash-based features can be derived. Biases can be introduced during this preprocessing because it is performed independently of the subsequent modeling task, which can result in the models constructed from the imputed hash-based features being suboptimal. To overcome this limitation, we present a novel framework called H-FLIP that simultaneously estimates the missing values while constructing a set of nonlinear hash-based features from the incomplete data. The effectiveness of the framework is demonstrated through experiments using both synthetic and real-world data sets.

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A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News

Cited by 150 publications

References 13 publications

Dropout-Based Active Learning for Regression

Dropout-Based Active Learning for Regression

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

Hash-Based Feature Learning for Incomplete Continuous-Valued Data

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