EMDID: Evolutionary multi-objective discretization for imbalanced datasets

Tahan, Marzieh Hajizadeh; Asadi, Shahrokh

doi:10.1016/j.ins.2017.12.023

Cited by 29 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secondly, experimental evidence supports that discretization can improve naive Bayes performance (Mubaroq et al, 2019). Tahan and Asadi (2018) also highlight that discretization can mitigate the impact of class imbalance on supervised learning. This is interesting because the class imbalance is a common condition in bioinformatics (Zhang et al, 2020).…”

Section: Background Reviewmentioning

confidence: 94%

See 1 more Smart Citation

Exploring conditional missing patterns for automated bacteria identification using MALDI-TOF MS data

Rocha,

Campos Jr.,

Etto

et al. 2023

Anais Do XIV Congresso Brasileiro De Agroinformática (SBIAGRO 2023)

View full text Add to dashboard Cite

Training classifiers for automated bacterial identification using MALDI-TOF fingerprints requires addressing class-conditional missingness patterns (CMPs). A CMP is a non-missing-at-random pattern that provides evidence for classification. One possible strategy to handle CMPs is feature stratification. This work evaluated the effectiveness of stratification in training naive Bayes classifiers for the proposed task through two experiments. The first experiment compared the predictive performance of categorical naive Bayes classifiers trained on stratified/discretized features with the performance of a Gaussian naive Bayes fitted on imputed data. The second experiment assessed the impact of class imbalance on the differences in the performance of Gaussian and categorical naive Bayes classifiers. The ANOVA results suggest that feature stratification can induce more accurate classifiers. Correlation analysis shows that class imbalance has a low influence on the difference in the performances of classifiers.

show abstract

Section: Background Reviewmentioning

confidence: 94%

“…Naive Bayes addresses this prerequisite because this is a common preprocessing step in its application (Yang and Webb, 2009). Additionally, as noted in Tahan and Asadi (2018), discretization can mitigate the impact of class imbalance on supervised learning. That is a common condition in bioinformatics (Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Exploring conditional missing patterns for automated bacteria identification using MALDI-TOF MS data

Rocha,

Campos Jr.,

Etto

et al. 2023

Anais Do XIV Congresso Brasileiro De Agroinformática (SBIAGRO 2023)

View full text Add to dashboard Cite

show abstract

“…The genetic algorithm has inherent implicit parallelism and strong global search ability, and has achieved promising results on the problem of feature discretization 47 . The genetic algorithm uses binary coding to encode the candidate breakpoints.…”

Section: Related Workmentioning

confidence: 99%

A deep network embedded with rough fuzzy discretization for OCT fundus image segmentation

Chen

Zeng

Lin

2023

Sci Rep

View full text Add to dashboard Cite

The noise and redundant information are the main reasons for the performance bottleneck of medical image segmentation algorithms based on the deep learning. To this end, we propose a deep network embedded with rough fuzzy discretization (RFDDN) for OCT fundus image segmentation. Firstly, we establish the information decision table of OCT fundus image segmentation, and regard each category of segmentation region as a fuzzy set. Then, we use the fuzzy c-means clustering to get the membership degrees of pixels to each segmentation region. According to membership functions and the equivalence relation generated by the brightness attribute, we design the individual fitness function based on the rough fuzzy set, and use a genetic algorithm to search for the best breakpoints to discretize the features of OCT fundus images. Finally, we take the feature discretization based on the rough fuzzy set as the pre-module of the deep neural network, and introduce the deep supervised attention mechanism to obtain the important multi-scale information. We compare RFDDN with U-Net, ReLayNet, CE-Net, MultiResUNet, and ISCLNet on the two groups of 3D retinal OCT data. RFDDN is superior to the other five methods on all evaluation indicators. The results obtained by ISCLNet are the second only inferior to those obtained by RFDDN. DSC, sensitivity, and specificity of RFDDN are evenly 3.3%, 2.6%, and 7.1% higher than those of ISCLNet, respectively. HD95 and ASD of RFDDN are evenly 6.6% and 19.7% lower than those of ISCLNet, respectively. The experimental results show that our method can effectively eliminate the noise and redundant information in Oct fundus images, and greatly improve the accuracy of OCT fundus image segmentation while taking into account the interpretability and computational efficiency.

show abstract

“…ML algorithms have been applied in flood susceptibility mapping [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], rainfall-runoff modeling [32,33], reservoir inflow forecasting [34,35], stream flow prediction [36,37], suspended sediment estimation [38,39] and the estimation of daily reference evapotranspiration [40,41]. Bayes-based algorithms, such as Bayesian logistic regression (BLR) and decision tree algorithms, such as random forest (RF), alternating decision tree (ADT), logistic model trees (LMT), naïve Bayes tree (NBT), reduced error pruning tree (REPTree) and classification and regression trees (CARTs), have been applied in water resource issues, especially in flood susceptibility mapping [18,[42][43][44][45]. Khosravi et al [46] developed a hybrid algorithm of bagging-decision tree for bed load transport rate prediction.…”

Section: Introductionmentioning

confidence: 99%

Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms

Nhu

Shahabi

Nohani

et al. 2020

IJGI

View full text Add to dashboard Cite

Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models’ performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.

show abstract

EMDID: Evolutionary multi-objective discretization for imbalanced datasets

Cited by 29 publications

References 35 publications

Exploring conditional missing patterns for automated bacteria identification using MALDI-TOF MS data

Exploring conditional missing patterns for automated bacteria identification using MALDI-TOF MS data

A deep network embedded with rough fuzzy discretization for OCT fundus image segmentation

Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms

Contact Info

Product

Resources

About