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IntelELM (Intelligent Metaheuristic-based Extreme Learning Machine) is a Python library that implements a frameworkfor training Extreme Learning Machine (ELM) networks using Metaheuristic Algorithms. It provides a comparablealternative to the traditional ELM network and is compatible with the Scikit-Learn library. With IntelELM, you canperform searches and hyperparameter tuning using the functionalities provided by the Scikit-Learn library.

• Free software: GNU General Public License (GPL) V3 license
• Provided Estimator: ElmRegressor, ElmClassifier, MhaElmRegressor, MhaElmClassifier, AutomatedMhaElmTuner, AutomatedMhaElmComparator
• Total Optimization-based ELM Regression: > 200 Models
• Total Optimization-based ELM Classification: > 200 Models
• Supported datasets: 54 (47 classifications and 7 regressions)
• Supported performance metrics: >= 67 (47 regressions and 20 classifications)
• Supported objective functions (as fitness functions or loss functions): >= 67 (47 regressions and 20 classifications)
• Documentation:https://intelelm.readthedocs.io/en/latest/
• Python versions: >= 3.7.x
• Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics

If you want to understand how Metaheuristic is applied to Extreme Learning Machine, you need to read the papertitled "A new workload prediction model using extreme learning machine and enhanced tug of war optimization".The paper can be accessed at the followingthis link

Please include these citations if you plan to use this library:

@article{van2025intelelm,title={IntelELM: A python framework for intelligent metaheuristic-based extreme learning machine},author={Van Thieu, Nguyen and Houssein, Essam H and Oliva, Diego and Hung, Nguyen Duy},journal={Neurocomputing},volume={618},pages={129062},year={2025},publisher={Elsevier},doi={10.1016/j.neucom.2024.129062}}@article{nguyen2020new,title={A new workload prediction model using extreme learning machine and enhanced tug of war optimization},author={Nguyen, Thieu and Hoang, Bao and Nguyen, Giang and Nguyen, Binh Minh},journal={Procedia Computer Science},volume={170},pages={362--369},year={2020},publisher={Elsevier},doi={10.1016/j.procs.2020.03.063}}@article{van2023mealpy,title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},author={Van Thieu, Nguyen and Mirjalili, Seyedali},journal={Journal of Systems Architecture},year={2023},publisher={Elsevier},doi={10.1016/j.sysarc.2023.102871}}

• Install thecurrent PyPI release:

$ pip install intelelm

After installation, you can check the version of current installed IntelELM by:

$ python>>> import intelelm>>> intelelm.__version__

• Tutorials can be found insidetutorials directory.
  • Building a handwritten digits classifier using IntelELM
  • Building a house price predictor using IntelELM

• In this section, we will explore the usage of the IntelELM model with the assistance of a dataset. While all thepreprocessing steps mentioned below can be replicated using Scikit-Learn, we have implemented some utility functionsto provide users with convenience and faster usage.

### Step 1: Importing the librariesfromintelelmimportElmRegressor,ElmClassifier,MhaElmRegressor,MhaElmClassifier,get_dataset#### Step 2: Reading the datasetdata=get_dataset("aniso")#### Step 3: Next, split dataset into train and test setdata.split_train_test(test_size=0.2,shuffle=True,random_state=100)#### Step 4: Feature Scalingdata.X_train,scaler_X=data.scale(data.X_train,scaling_methods=("standard","minmax"))data.X_test=scaler_X.transform(data.X_test)data.y_train,scaler_y=data.encode_label(data.y_train)# This is for classification problem onlydata.y_test=scaler_y.transform(data.y_test)#### Step 5: Fitting ELM-based model to the dataset##### 5.1: Use standard ELM model for regression problemregressor=ElmRegressor(layer_sizes=(10, ),act_name="relu",seed=42)regressor.fit(data.X_train,data.y_train)##### 5.2: Use standard ELM model for classification problemclassifer=ElmClassifier(layer_sizes=(10, ),act_name="tanh",seed=42)classifer.fit(data.X_train,data.y_train)##### 5.3: Use Metaheuristic-based ELM model for regression problemprint(MhaElmClassifier.SUPPORTED_OPTIMIZERS)print(MhaElmClassifier.SUPPORTED_REG_OBJECTIVES)opt_paras= {"name":"GA","epoch":10,"pop_size":30}regressor=MhaElmRegressor(layer_sizes=(10, ),act_name="elu",obj_name="RMSE",optim="BaseGA",optim_paras=opt_paras,seed=42)regressor.fit(data.X_train,data.y_train)##### 5.4: Use Metaheuristic-based ELM model for classification problemprint(MhaElmClassifier.SUPPORTED_OPTIMIZERS)print(MhaElmClassifier.SUPPORTED_CLS_OBJECTIVES)opt_paras= {"name":"GA","epoch":10,"pop_size":30}classifier=MhaElmClassifier(layer_sizes=(10, ),act_name="elu",obj_name="KLDL",optim="BaseGA",optim_paras=opt_paras,seed=42)classifier.fit(data.X_train,data.y_train)#### Step 6: Predicting a new resulty_pred=regressor.predict(data.X_test)y_pred_cls=classifier.predict(data.X_test)y_pred_label=scaler_y.inverse_transform(y_pred_cls)#### Step 7: Calculate metrics using score or scores functions.print("Try my AS metric with score function")print(regressor.score(data.X_test,data.y_test,method="AS"))print("Try my multiple metrics with scores function")print(classifier.scores(data.X_test,data.y_test,list_methods=["AS","PS","F1S","CEL","BSL"]))print("Try my evaluate functions")print(regressor.evaluate(data.y_test,y_pred,list_metrics=("RMSE","MAE","MAPE","NSE","R2")))#### Save resultsregressor.save_loss_train(save_path="history",filename="loss_train.csv")regressor.save_metrics(data.y_test,y_pred,list_metrics=("R2","MAPE","MAE","MSE"),save_path="history",filename="metrics.csv")

A real-world dataset contains features that vary in magnitudes, units, and range. We would suggest performingnormalization when the scale of a feature is irrelevant or misleading. Feature Scaling basically helps to normalizethe data within a particular range.

1. Where do I find the supported metrics like above ["AS", "PS", "RS"]. What is that?You can find it here:https://github.com/thieu1995/permetrics or use this

fromintelelmimportMhaElmClassifier,MhaElmRegressorprint(MhaElmRegressor.SUPPORTED_REG_OBJECTIVES)print(MhaElmClassifier.SUPPORTED_CLS_OBJECTIVES)

2. I got this type of error

raiseValueError("Existed at least one new label in y_pred.")ValueError:Existedatleastonenewlabeliny_pred.

How to solve this?

• This occurs only when you are working on a classification problem with a small dataset that has many classes. Forinstance, the "Zoo" dataset contains only 101 samples, but it has 7 classes. If you split the dataset into atraining and testing set with a ratio of around 80% - 20%, there is a chance that one or more classes may appearin the testing set but not in the training set. As a result, when you calculate the performance metrics, you mayencounter this error. You cannot predict or assign new data to a new label because you have no knowledge about thenew label. There are several solutions to this problem.

• 1st: Use the SMOTE method to address imbalanced data and ensure that all classes have the same number of samples.

importpandasaspdfromimblearn.over_samplingimportSMOTEfromintelelmimportDatadataset=pd.read_csv('examples/dataset.csv',index_col=0).valuesX,y=dataset[:,0:-1],dataset[:,-1]X_new,y_new=SMOTE().fit_resample(X,y)data=Data(X_new,y_new)

• 2nd: Use different random_state numbers in split_train_test() function.

importpandasaspdfromintelelmimportDatadataset=pd.read_csv('examples/dataset.csv',index_col=0).valuesX,y=dataset[:,0:-1],dataset[:,-1]data=Data(X,y)data.split_train_test(test_size=0.2,random_state=10)# Try different random_state value

3. When testing several algorithms based on Extreme Learning Machines (ELM), they all produce the same results.Even during the training process, the global best solution remains unchanged.

• This issue was identified in version <= v1.0.2 when the default values for the lower bound (lb) and upper bound(ub) were set in the narrow range of (-1, 1). This limited range proved to be too small, causing all algorithms toconverge to local optima. Fortunately, this problem has been addressed in versions > v1.0.3, where the defaultrange has been extended to (-10., 10.). You also can define your own lb and ub ranges depend on your problem.
• In traditional neural network like MLP, they weights (weights + biases) are typically initialized within the rangeof (-1., 1.). However, during training using gradient-based methods, these values are updated, and there are nostrict bounds on them.
• Meanwhile, in metaheuristic optimization, it's necessary to set boundaries for decision variables (weights) eachtime a new search agent is formed. Therefore, if you define a narrow range, your optimizer may converge morequickly, but it's more likely to get stuck in local optima (which explains why the global best value remainsunchanged during training). Moreover, in some cases, there might not even be a global optimum within that narrowrange. Conversely, if you set a wider range, the optimization process may be slower, and the global best value maychange more gradually. In such cases, you might need to increase the number of epochs, perhaps up to 1000, for theoptimizer to explore the solution space thoroughly.

fromintelelmimportMhaElmClassifieropt_paras= {"name":"GA","epoch":30,"pop_size":30}model=MhaElmClassifier(layer_sizes=(10, ),act_name="elu",obj_name="KLDL",optim="BaseGA",optim_paras=opt_paras,verbose=True,seed=42)model.fit(X_train,y_train,lb=(-10., ),ub=(10., ))y_pred=model.predict(X_test)

• Official source code repo:https://github.com/thieu1995/intelelm

• Official document:https://intelelm.readthedocs.io/

• Download releases:https://pypi.org/project/intelelm/

• Issue tracker:https://github.com/thieu1995/intelelm/issues

• Notable changes log:https://github.com/thieu1995/intelelm/blob/master/ChangeLog.md

• Official chat group:https://t.me/+fRVCJGuGJg1mNDg1

• This project also related to our another projects which are "optimization" and "machine learning", check it here:

  • https://github.com/thieu1995/mealpy
  • https://github.com/thieu1995/metaheuristics
  • https://github.com/thieu1995/opfunu
  • https://github.com/thieu1995/enoppy
  • https://github.com/thieu1995/permetrics
  • https://github.com/thieu1995/MetaCluster
  • https://github.com/thieu1995/pfevaluator
  • https://github.com/aiir-team

1. https://analyticsindiamag.com/a-beginners-guide-to-extreme-learning-machine/
2. https://medium.datadriveninvestor.com/extreme-learning-machine-for-simple-classification-e776ad797a3c
3. https://www.extreme-learning-machines.org/
4. https://web.njit.edu/~usman/courses/cs675_fall20/ELM-NC-2006.pdf
5. https://m-clark.github.io/models-by-example/elm.html
6. https://github.com/ivallesp/simplestELM
7. https://www.javatpoint.com/elm-in-machine-learning