Simplified Swarm Optimization with Sorted Local Search for golf data classification

Liu, Yao; Chung, Yuk Ying; Yeh, Wei Chang

doi:10.1109/cec.2012.6256606

Cited by 8 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[44] Concluded that the most important elements in basketball are two-point shots under the arch and defensive rebound. [29] A data mining approach for classification and identification of golf swing from weight shift data.…”

Section: [40]mentioning

confidence: 99%

See 1 more Smart Citation

Computational Intelligence in Sports: A Systematic Literature Review

Bonidia

Rodrigues

Avila-Santos

et al. 2018

Advances in Human-Computer Interaction

View full text Add to dashboard Cite

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

show abstract

Section: [40]mentioning

confidence: 99%

“…[44] Artificial Neural Networks. [29] Particle Swarm Optimization, Support Vector Machine, C4.5. [52] Bayesian Belief Networks, Naive Bayes and K-means.…”

Section: Rq7: Appliedmentioning

confidence: 99%

Computational Intelligence in Sports: A Systematic Literature Review

Bonidia

Rodrigues

Avila-Santos

et al. 2018

Advances in Human-Computer Interaction

View full text Add to dashboard Cite

show abstract

“…Disassembly Sequencing Problems have been solved with SSO in [43], [45], [46]. The mining of classification rules was investigated in [4], [25], [44]. Image classification with SSO has been studied in [34].…”

Section: B Simplified Swarm Optimizationmentioning

confidence: 99%

Simple Probabilistic Population-Based Optimization

Lin

Claub

Middendorf

2016

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

A generic scheme is proposed for designing and classifying simple probabilistic population based optimization algorithms that use principles from Population-based Ant Colony Optimization and Simplified Swarm Optimization for solving combinatorial optimization problems. The scheme, called Simple Probabilistic Population Based Optimization scheme, identifies different types of populations (or archives) and their influence on the construction of new solutions. The scheme is used to show how Simplified Swarm Optimization can be adapted for solving combinatorial optimization problems and how it is related to Population-based Ant Colony Optimization. Moreover, several new variants and combinations of these two metaheuristics are generated with the proposed scheme. An experimental study is done to evaluate and compare the influence of different population types on the optimization behaviour of Simple Probabilistic Population Based Optimization algorithms, when applied to the Traveling Salesperson Problem and the Quadratic Assignment Problem.

show abstract

“…The main strength is that they are parallel in nature [17] GAs and PSO are commonly associated with the optimization of continuous numerical functions, and ACO with combinatorial optimization [16]. Some of the benefits of adopting such techniques are flexibility in retraining, online/continuous learning and the potential for parallelism in the algorithms, which can be exploited both in the training and detection process.…”

Section: Swarm Intelligence In Intrusion Detectionmentioning

confidence: 99%

Data Preprocessing for Intrusion Detection System using Swarm Intelligence Techniques

Revathi¹,

Malathi²

2013

IJCA

View full text Add to dashboard Cite

Due to access of malicious data in internet, Intrusion detection system becomes an important element in system security that controls real time data and leads to huge dimensional problem, so a data pre-processing is necessary to reduce haziness and to clean network data. To reduce false positive rate and to increase efficiency of detection, the paper proposed a new swarm intelligence technique to solve complex optimization problem. The paper work based on hybrid Simplified Swarm Optimization (SSO) algorithm to pre-process the data. SSO is a simplified Particle Swarm Optimization (PSO) that has a self-organizing ability to emerge in highly distributed control problem area, and is versatile, strong and cost effective to resolve complex computing environments. It recognize not only known attacks but also filters noisy and irrelevant data that may result on knowledge Discovery and Data Mining (KDDCup 1999) dataset and compared to a new hybrid Partial Swarm Optimization with Random Forest (PSO-RF) and with other benchmark classifiers. The testing result shows that the proposed method provides competitively high detection rates and produce a near optimal solution.

show abstract

Simplified Swarm Optimization with Sorted Local Search for golf data classification

Cited by 8 publications

References 22 publications

Computational Intelligence in Sports: A Systematic Literature Review

Computational Intelligence in Sports: A Systematic Literature Review

Simple Probabilistic Population-Based Optimization

Data Preprocessing for Intrusion Detection System using Swarm Intelligence Techniques

Contact Info

Product

Resources

About