2.2k citations · 3.2k across the 37 of their papers we have counts for
16 papers · 1 filter
Randomized Neural Network with Adaptive Forward Regularization for Online Task-free Class Incremental Learning
Junda Wang, Minghui Hu, Ning Li +2
Class incremental learning (CIL) requires an agent to learn distinct tasks consecutively with knowledge retention against forgetting. Problems impeding the practical applications o…
Multi-Scale Finetuning for Encoder-based Time Series Foundation Models
Zhongzheng Qiao, Chenghao Liu, Yiming Zhang +6
Time series foundation models (TSFMs) demonstrate impressive zero-shot performance for time series forecasting. However, an important yet underexplored challenge is how to effectiv…
R^2VFL: A Robust Random Vector Functional Link Network with Huber-Weighted Framework
Anuradha Kumari, Mushir Akhtar, P. N. Suganthan +1
The random vector functional link (RVFL) neural network has shown significant potential in overcoming the constraints of traditional artificial neural networks, such as excessive c…
Incremental Online Learning of Randomized Neural Network with Forward Regularization
Junda Wang, Minghui Hu, Ning Li +2
Online learning of deep neural networks suffers from challenges such as hysteretic non-incremental updating, increasing memory usage, past retrospective retraining, and catastrophi…
Vanilla Gradient Descent for Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran +1
Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, es…
Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System
M. Sajid, M. Tanveer, P. N. Suganthan
The ensemble deep random vector functional link (edRVFL) neural network has demonstrated the ability to address the limitations of conventional artificial neural networks. However,…
Class-incremental Learning for Time Series: Benchmark and Evaluation
Zhongzheng Qiao, Quang Pham, Zhen Cao +4
Real-world environments are inherently non-stationary, frequently introducing new classes over time. This is especially common in time series classification, such as the emergence…
IF2Net: Innately Forgetting-Free Networks for Continual Learning
Depeng Li, Tianqi Wang, Bingrong Xu +3
Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which inform…
Heterogeneous Oblique Double Random Forest
M. A. Ganaie, M. Tanveer, I. Beheshti +2
The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the dat…
SFE: A Simple, Fast and Efficient Feature Selection Algorithm for High-Dimensional Data
Behrouz Ahadzadeh, Moloud Abdar, Fatemeh Safara +3
In this paper, a new feature selection algorithm, called SFE (Simple, Fast, and Efficient), is proposed for high-dimensional datasets. The SFE algorithm performs its search process…
Ensemble Reinforcement Learning: A Survey
Yanjie Song, P. N. Suganthan, Witold Pedrycz +4
Reinforcement Learning (RL) has emerged as a highly effective technique for addressing various scientific and applied problems. Despite its success, certain complex tasks remain ch…
Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification
Qiushi Shi, Ponnuthurai Nagaratnam Suganthan, Rakesh Katuwal
In this paper, we first introduce batch normalization to the edRVFL network. This re-normalization method can help the network avoid divergence of the hidden features. Then we prop…
Oblique and rotation double random forest
M. A. Ganaie, M. Tanveer, P. N. Suganthan +1
Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among…
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Ruobin Gao, Liang Du, P. N. Suganthan +2
Electricity load forecasting is crucial for the power systems' planning and maintenance. However, its un-stationary and non-linear characteristics impose significant difficulties i…
Ensemble deep learning: A review
M. A. Ganaie, Minghui Hu, A. K. Malik +2
Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning architectures are showing better performance compared to…
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces
Rakesh Katuwal, P. N. Suganthan
Both neural networks and decision trees are popular machine learning methods and are widely used to solve problems from diverse domains. These two classifiers are commonly used bas…