Novel ensemble methods for regression via classification problems

Ahmad, Amir; Halawani, Sami M.; Albidewi, Ibrahim

doi:10.1016/j.eswa.2011.12.029

Cited by 10 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed approach involves approximating the posterior through discrete categorical distributions, subsequently solving a regression problem via classification. This approach in similar form has delivered encouraging results (Weiss & Indurkhya, 1995;Torgo & Gama, 1996;Ahmad et al, 2012).…”

Section: Introductionmentioning

confidence: 94%

Robust and integrative Bayesian neural networks for likelihood-free parameter inference

Wrede¹,

Eriksson²,

Jiang³

et al. 2021

Preprint

View full text Add to dashboard Cite

State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihoodfree parameter inference. Existing approaches require density estimation as a post-processing step building upon deterministic neural networks, and do not take network prediction uncertainty into account. This work proposes a robust integrated approach that learns summary statistics using Bayesian neural networks, and directly estimates the posterior density using categorical distributions. An adaptive sampling scheme selects simulation locations to efficiently and iteratively refine the predictive posterior of the network conditioned on observations. This allows for more efficient and robust convergence on comparatively large prior spaces. We demonstrate our approach on benchmark examples and compare against related methods.

show abstract

Section: Introductionmentioning

confidence: 94%

Robust and integrative Bayesian neural networks for likelihood-free parameter inference

Wrede¹,

Eriksson²,

Jiang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Ahmad A. proposed an ensemble model by Extreme Randomized Discretisation (ERD) for discretizing the target continuous attribute into class intervals. The authors proved that the ensembles for regression via classification performed better than regression via classification with the equal width discretisation method [16].…”

Section: Related Workmentioning

confidence: 99%

Numeric Prediction of Dissolved Oxygen Status Through Two-Stage Training for Classification-Driven Regression

Guo

Liu

et al. 2019

2019 International Conference on Machine Learning and Cybernetics (ICMLC)

View full text Add to dashboard Cite

Dissolved oxygen of aquaculture is an important measure of the quality of culture environment and how aquatic products have been grown. In the machine learning context, the above measure can be achieved by defining a regression problem, which aims at numerical prediction of the dissolved oxygen status. In general, the vast majority of popular machine learning algorithms were designed for undertaking classification tasks. In order to effectively adopt the popular machine learning algorithms for the above-mentioned numerical prediction, in this paper, we propose a two-stage training approach that involves transforming a regression problem into a classification problem and then transforming it back to regression problem. In particular, unsupervised discretization of continuous attributes is adopted at the first stage to transform the target (numeric) attribute into a discrete (nominal) one with several intervals, such that popular machine learning algorithms can be used to predict the interval to which an instance belongs in the setting of a classification task. Furthermore, based on the classification result at the first stage, some of the instances within the predicted interval are selected for training at the second stage towards numerical prediction of the target attribute value of each instance. An experimental study is conducted to investigate in general the effectiveness of the popular learning algorithms in the numerical prediction task and also analyze how the increase of the number of training instances (selected at the second training stage) can impact on the final prediction performance. The results show that the adoption of decision tree learning and neural networks lead to better and more stable performance than Naive Bayes, K Nearest Neighbours and Support Vector Machine.

show abstract

“…Fida et al (2011) proposed an ensemble algorithm to enhance the decision of the classification algorithms in diagnosis of heart diseases and they could produce an algorithm with an optimal result. Ahmad et al (2012) presented a novel ensemble approach based on seeking an optimal combination of multiple algorithms to raise the rate of correct decisions in solving regression problems. Peng (2006) also proposed an ensemble algorithm which began to generate a pool of candidate base classifiers according to the gene sub-samplings, then the process of selection of a subset of appropriate base classification algorithms starts to construct the classification committee based on classifier clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

Boosting-BoW Algorithm for Finding Kidney Diseases from Medical Test Reports

Qader¹,

Ali²

2019

Journal of Computer Science

View full text Add to dashboard Cite

This paper introduces an approach to increase the accuracy rate of classification by employing Bag-of-Words (BoW) as a feature selection method along with machine learning algorithms to obtain a more accurate output. Because of its capability in quickly processing large sets of data and getting accurate results, this approach can be used in medical areas. Different ensemble approaches are generated by different researchers to obtain good results as mentioned in the literature review. In this study a novel algorithm is proposed to analyze medical kidney test reports, using BoW for selecting the features and analyzing them via Boosting four different machine learning classification algorithms like Sequential Minimum Optimization (SMO), k-Nearest Neighbors (k-NN), Random Forests (RF) and Naïve Bayes (NB). With the help of specialists in urology, the proposed algorithm is tested against multiple datasets of different kidney tests. The accuracy of the proposed Boosting algorithms outperforms its counterpart algorithms like SMO, k-NN, RF and NB when they had showen their performances alone.

show abstract

Novel ensemble methods for regression via classification problems

Cited by 10 publications

References 19 publications

Robust and integrative Bayesian neural networks for likelihood-free parameter inference

Robust and integrative Bayesian neural networks for likelihood-free parameter inference

Numeric Prediction of Dissolved Oxygen Status Through Two-Stage Training for Classification-Driven Regression

Boosting-BoW Algorithm for Finding Kidney Diseases from Medical Test Reports

Contact Info

Product

Resources

About