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Engineering
English
Computational Intelligence
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Book Series
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Chapter
Introduction
This book offers a new investment strategy that involves identifying “Outperforming” and “Underperforming” stocks from the S &P 500 index based on an ensemble of machine learning algorithms. This strategy uses...
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Chapter
Methodology
Chapter three of the book describes the methodological framework laying out the system’s architecture and the data processing pipeline from acquisition to analysis. It describes the selection of financial indi...
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Chapter
Conclusion
The final chapter summarizes the research results, highlighting that the ensemble method outperforms both individual models and the S&P 500 index benchmark in risk-adjusted returns. It reflects on the study’s ...
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Book
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Chapter
State-of-the-Art
The second chapter presents a holistic review of the fundamental concepts and literature that are prerequisites for the main research. It starts with a brief description of the structure of the stock market an...
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Chapter
System Validation
This chapter presents the validation of the investment strategy developed in the book. It carefully examines the performance of the ensemble method in classifying stocks and evaluates the results of the invest...
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Article
Game-theoretic multi-agent motion planning in a mixed environment
The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled robots are present in a mixed environment. To address this challenge, this paper presents an...
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Article
A general framework for improving cuckoo search algorithms with resource allocation and re-initialization
Cuckoo search (CS) has currently become one of the most favorable meta-heuristic algorithms (MHAs). In this article, a simple yet effective framework is proposed for CS algorithms to reinforce their performanc...
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Article
Tensor discriminant analysis on grassmann manifold with application to video based human action recognition
Representing videos as linear subspaces on Grassmann manifolds has made great strides in action recognition problems. Recent studies have explored the convenience of discriminant analysis by making use of Gras...
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Article
ConDA: state-based data augmentation for context-dependent text-to-SQL
The context-dependent text-to-SQL task has profound real-world implications, as it facilitates users in extracting knowledge from vast databases, which allows users to acquire the information interactively for...
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Article
Fast Shrinking parents-children learning for Markov blanket-based feature selection
High-dimensional data leads to degraded performance of machine learning algorithms and weak generalization of models, so feature selection is of great importance. In a Bayesian network (BN), the Markov blanket...
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Article
Open AccessDistributed order estimation for continuous-time stochastic systems
In this paper, we investigate the distributed estimation problem of continuous-time stochastic dynamic systems over sensor networks when both the system order and parameters are unknown. We propose a local inf...
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Article
Combining core points and cluster-level semantic similarity for self-supervised clustering
Contrastive learning utilizes data augmentation to guide network training. This approach has attracted considerable attention for clustering, object detection, and image segmentation. However, previous studies...
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Article
Drfnet: dual stream recurrent feature sharing network for video dehazing
The primary effects of haze on captured images/frames are visibility degradation and color disturbance. Even though extensive research has been done on the tasks of video dehazing, they fail to perform better ...
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Article
Open AccessSliced Wasserstein adversarial training for improving adversarial robustness
Recently, deep-learning-based models have achieved impressive performance on tasks that were previously considered to be extremely challenging. However, recent works have shown that various deep learning model...
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Article
Online distributed optimization with stochastic gradients: high probability bound of regrets
In this paper, the problem of online distributed optimization subject to a convex set is studied via a network of agents. Each agent only has access to a noisy gradient of its own objective function, and can c...
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Article
Aspect category sentiment classification via document-level GAN and POS information
The purpose of aspect-category sentiment classification (ACSC) is to determine the sentiment polarity of the predefined aspect category from the texts. Current methods for ACSC have two main limitations. Since...
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Article
Data-driven quantification and intelligent decision-making in traditional Chinese medicine: a review
Traditional Chinese medicine (TCM) originates from the practical experience of human beings’ constant struggle with nature. In five thousand years, TCM has gradually risen from empirical medicine to modern evi...
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Article
BPSO-SLM: a binary particle swarm optimization-based self-labeled method for semi-supervised classification
The self-labeled methods have been favored by scholars in semi-supervised classification. Mislabeling is a great challenge for self-labeled methods and one of the reasons for mislabeling is that high-confidenc...
