BCS: A Binary Cuckoo Search algorithm for feature selection

Rodrigues, Douglas; Pereira, Luiz Haroldo; Almeida, T. N. S.; Papa, João Paulo; Souza, Ana Cristina de; Ramos, Caio C. O.; Yang, Xin-She

doi:10.1109/iscas.2013.6571881

Cited by 158 publications

(111 citation statements)

References 10 publications

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“…Table 2 shows that FS-BCS achieves better SR% without significantly reducing the classification accuracy in lesser number of iterations compared to the Genetic and PSO approaches. The main reason for these results is that BCS's convergence is more efficient and faster compared to convergence of the PSO and Genetic approaches, because BCS uses efficient and simple hybrid mechanism [20] [19]. To be more specific, the local search in the BCS's hybrid mechanism plays the main role in the efficiency and speed of its convergence, because it uses levy flight technique to maximize the guarantee and speed of convergence [35].…”

Section: Resultsmentioning

confidence: 99%

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Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

Alia¹,

Taweel²

2017

IJITCS

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Abstract-Feature Selection (FS) is an important process to find the minimal subset of features from the original data by removing the redundant and irrelevant features. It aims to improve the efficiency of classification algorithms. Rough set theory (RST) is one of the effective approaches to feature selection, but it uses complete search to search for all subsets of features and dependency to evaluate these subsets. However, the complete search is expensive and may not be feasible for large data due to its high cost. Therefore, meta-heuristics algorithms, especially Nature Inspired Algorithms, have been widely used to replace the reduction part in RST. This paper develops a new algorithm for Feature Selection based on hybrid Binary Cuckoo Search and rough set theory for classification on nominal datasets. The developed algorithm is evaluated on five nominal datasets from the UCI repository, against a number of similar NIAs algorithms. The results show that our algorithm achieves better FS compared to two known NIAs in a lesser number of iterations, without significantly reducing the classification accuracy.

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Section: Resultsmentioning

confidence: 99%

“…BCS is proposed by [19] binary n-bit string, where n is the number of features [19]. BCS represents each nest as a binary vector, where each 1 corresponds to a selected feature and 0 otherwise.…”

Section: Binary Cuckoo Searchmentioning

confidence: 99%

Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

Alia¹,

Taweel²

2017

IJITCS

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“…Several works have used this technique [24,53,74,81,87]. The mapping within this technique is performed over two steps.…”

Section: Sigmoid Functionmentioning

confidence: 99%

“…A binary Harmony Search (BHS) algorithm was also proposed in [21]. Recently, the authors in [24,34,76,81] proposed binary versions of the bat algorithm (BBA), the FireFlyAlgorithm (BFFA), the Cuckoo Search (BCS) and the Artificial Bee Colony (BABC) algorithm, respectively.…”

Section: Introduction Q4mentioning

confidence: 99%

On the efficiency of the binary flower pollination algorithm: Application on the antenna positioning problem

Dahi

Mezioud

Draa

2016

Applied Soft Computing

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“…Such approaches can be useful to the application of evolutionary optimization techniques to solve complex tasks. The readers can refer to some recent literature such as the Binary Particle Swarm Optimization (BPSO) [5], Binary Firefly Algorithm (BFA) [4], Binary Harmony Search (BHS) [14], Binary Gravitational Search Algorithm (BGSA) [16], Binary Cuckoo Search (BCS) [17], Binary Charged System Search (BCSS) [19], and Binary Bat Algorithm (BBA) [18].…”

Section: Introductionmentioning

confidence: 99%

Binary Flower Pollination Algorithm and Its Application to Feature Selection

Rodrigues

Yang

Souza

et al. 2014

Studies in Computational Intelligence

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The problem of feature selection has been paramount in the last years, since it can be as important as the classification step itself. The main goal of feature selection is to find out the subset of features that optimize some fitness function, often in terms of a classifier's accuracy or even the computational burden for extracting each feature. Therefore, the approaches to feature selection can be modeled as optimization tasks. In this chapter, we evaluate a binary-constrained version of the Flower Pollination Algorithm (FPA) for feature selection, in which the search space is a boolean lattice where each possible solution, or a string of bits, denotes whether a feature will be used to compose the final set. Numerical experiments over some public and private datasets have been carried out and comparison with Particle Swarm Optimization, Harmony Search and Firefly Algorithm has demonstrated the suitability of the FPA for feature selection.

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BCS: A Binary Cuckoo Search algorithm for feature selection

Cited by 158 publications

References 10 publications

Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets

On the efficiency of the binary flower pollination algorithm: Application on the antenna positioning problem

Binary Flower Pollination Algorithm and Its Application to Feature Selection

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