Online estimation method for extreme learning machine with kernels based on the multi-innovation theory and
Yanjiao Wang1, Yiting Liu1, Weidi Li1
1School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
ISA Transactions
|November 13, 2024
Summary
This study introduces novel online learning models, the multi-innovation online sequential extreme learning machine (MIOSELM) and its kernel version (MIKOSELM), for dynamic data modeling. These methods enhance adaptability and efficiency in online computing, validated on benchmark datasets.
Area of Science:
- Machine Learning
- Computational Intelligence
- Data Science
Background:
- Effective online data modeling requires high adaptability to dynamic data and low computational complexity.
- Existing online learning models often struggle to balance adaptability with computational efficiency.
Purpose of the Study:
- To propose novel online sequential extreme learning machine models for improved online data modeling.
- To enhance model adaptability and computational efficiency for dynamic datasets.
Main Methods:
- Introduction of the multi-innovation online sequential extreme learning machine (MIOSELM) and its kernel version (MIKOSELM).
- Application of the multi-innovation theory using the latest 'p' samples for online estimation.
- Optimization of algorithm parameters and automatic search for 'p' using a modified whale optimization algorithm (MWOA).
Main Results:
- MIKOSELM achieved high accuracy (98.25%), F-score (98.11%), and G-mean (98.63%) on the WDBC dataset (UCI).
- On the KDD99 dataset, MIKOSELM demonstrated notable performance with 83.61% accuracy, 75.96% F-score, and 70.97% G-mean.
- MIKOSELM with MWOA achieved an F-score of 94.28% on Musk (UCI) and 76.73% on KDD99, validating its effectiveness.
Conclusions:
- The proposed MIOSELM and MIKOSELM effectively model dynamic data with enhanced adaptability and low complexity.
- The integration of MWOA further optimizes performance by tuning parameters and selecting appropriate sample sizes.
- Experimental results confirm the superiority of the proposed methods for online learning tasks.
Keywords:
Kernel extreme learning machineMulti-innovation theoryOnline learning modelParameter estimationSwarm intelligence algorithmMore Related Videos
13:54A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
Published on: August 18, 2023
4.4K
08:47Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
Published on: February 9, 2024
1.3K
Related Concept Videos
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
398
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
398
01:22Multi-input and Multi-variable systems
98
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
98
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
42
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
42
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
01:14Decision Making: Traditional Method
4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
01:16Response Surface Methodology
91
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
91