MEMOD: a novel multivariate evolutionary multi-objective discretization

Tahan, Marzieh Hajizadeh; Asadi, Shahrokh

doi:10.1007/s00500-016-2475-5

Cited by 21 publications

(9 citation statements)

References 56 publications

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“…Reference [ 38 ] proposed an evolutionary approach, which obtains a set of discretization schemes guiding the search by using a discretization criterion and the prediction accuracy of Naive Bayes. In Reference [ 39 ], classification error and number of cut points are simultaneously reduced by using evolutionary multi-objective optimization.…”

Section: Backgroundsmentioning

confidence: 99%

Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Jun

2021

Sensors

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Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.

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Section: Backgroundsmentioning

confidence: 99%

Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Jun

2021

Sensors

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“…To discretize the data ets with reference discretization methods, KEEL tool (http://sci2s.ugr.es/keel/download.php) is used. During our literature review, we observed that several studies report classification accuracy of their proposed methods using C4.5 and naive Bayes classifiers [5,12,[17][18][19][20]. For this reason, in this study, predictive accuracy of lFIT is also evaluated using these two classifiers of the Weka tool (https://www.cs.waikato.ac.nz/ml/weka/).…”

Section: Datasets and Experimental Settingsmentioning

confidence: 99%

“…To evaluate the performance of the proposed method, ten benchmark classification datasets are discretized with two unsupervised discretization methods, namely equal-width (EW), equal-frequency (EF), six supervised discretization methods, namely 1R [11], class-attribute contingency coefficient (CACC) [12], class-attribute interdependence maximization (CAIM) [13], MODL [14], MDLP [15], Hellinger [16], and the proposed method and the number of inconsistency and the number of cut-points the methods generated are compared. To evaluate predictive accuracy, similar to [5,12,[17][18][19][20], C4.5 and naive Bayes classifiers are trained with each discretized scheme and the predictive accuracy of the methods are compared. The experimental results show that lFIT ranks among top methods in predictive accuracy and generates schemes with low number of inconsistency and larger number of intervals.…”

Section: Introductionmentioning

confidence: 99%

lFIT: an unsupervised discretization method based on the Ramer–Douglas–Peucker algorithm

Mutlu¹,

Göz²,

Akbulut³

2019

Turk J Elec Eng & Comp Sci

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Discretization is the process of converting continuous values into discrete values. It is a preprocessing step of several machine learning and data mining algorithms and the quality of discretization may drastically affect the performance of these algorithms. In this study we propose a discretization algorithm, namely line fitting-based discretization (lFIT), based on the Ramer-Douglas-Peucker algorithm. It is a static, univariate, unsupervised, splittingbased, global, and incremental discretization method where intervals are determined based on the Ramer-Douglas-Peucker algorithm and the quality of partitioning is assessed based on the standard error of the estimate. To evaluate the performance of the proposed method, a set of experiments are conducted on ten benchmark datasets and the achieved results are compared to those obtained by eight state-of-the-art discretization methods. Experimental results show that lFIT achieves higher predictive accuracy and produces less number of inconsistency while it generates larger number of intervals. The obtained results are also validated through Friedman's test and Holm's post hoc test which revealed the fact that lFIT produces discretization schemes that statistically comply both with supervised and unsupervised discretization methods.

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“…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

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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.

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MEMOD: a novel multivariate evolutionary multi-objective discretization

Cited by 21 publications

References 56 publications

Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

lFIT: an unsupervised discretization method based on the Ramer–Douglas–Peucker algorithm

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

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