Feature selection with Intelligent Dynamic Swarm and Rough Set

Bae, Changseok; Yeh, Wei-Chang; Chung, Yuk Ying; Liu, Sin-Long

doi:10.1016/j.eswa.2010.03.016

Cited by 89 publications

(43 citation statements)

References 11 publications

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“…This hybrid algorithm uses the ability of SA to avoid being trapped in a local minimum and high rate convergence of the crossover operator of GA. PSO algorithm is also attractive for feature selection in that particles will discover best feature combinations as they fly within the subset space. In this way, Bae et al [87] have proposed a modified PSO called intelligent dynamic swarm (IDS). Niu et al [88] have introduced a hybrid optimizing algorithm, called AFSA-TSGM.…”

Section: Related Workmentioning

confidence: 99%

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Sheikhan

Mohammadi

2012

Neural Comput & Applic

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Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field.

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Section: Related Workmentioning

confidence: 99%

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Sheikhan

Mohammadi

2012

Neural Comput & Applic

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“…CREDIT 20 8 (3) 9 (15) 10 (2) 8 (16) 9 (4) 8 (20) 8 (19) 9 (1)) DERM2 34 9 (16) 10 (4) 8 (10) 9 (10) 8 (5) 9 (15) 8 (12) 9 (8) WQ 38 13 (4) 14 (15) 15 (1) 13 (16) 14 (4) 12 (13) 13 (7) 12 (7) 13 (11) 14 (2) …”

Section: Rsfs Resultsunclassified

“…Obviously, this is an exhaustive search, and is impractical for large data sets [70]. To manage the complexity of the search process, several stochastic optimization methods, such as hill climbing with forward selection and backward elimination [62] and meta-heuristic methods such as the genetic algorithm [62], particle swarm optimization [7,65], ant colony optimization [22,71], as well as great deluge and non-linear great deluge [1,34] can be used. In this study, we have used MRMC-IWD and hybrid MRMC-IWD to tackle this challenging problem.…”

Section: The Rough Set Feature Subset Selection (Rsfs) Problemmentioning

confidence: 99%

An ensemble of intelligent water drop algorithms and its application to optimization problems

Alijla

Wong

Lim

et al. 2015

Information Sciences

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“…Furthermore, an information system can have more than one reduced attribute set. The set obtained from the intersection of the reduced sets derived from an information system is called a core attribute set of the A attribute set [21,22]. The core attribute set can also be derived from the discernibility matrix.…”

Section: Attribute Reduction and Core Attributesmentioning

confidence: 99%

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Kaya¹

2013

Turk J Elec Eng & Comp Sci

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Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing the proposed hybrid model and the success rate of the RS + ELM model was determined as 100%. Moreover, the most appropriate attributes for the diagnosis of breast cancer were determined from the WBCD in this study. It is considered that the proposed method will be useful in similar medical practices.

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Feature selection with Intelligent Dynamic Swarm and Rough Set

Cited by 89 publications

References 11 publications

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

An ensemble of intelligent water drop algorithms and its application to optimization problems

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

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