Late Acceptance Hill Climbing Based Social Ski Driver Algorithm for Feature Selection

Chatterjee, Bitanu; Bhattacharyya, Trinav; Ghosh, Kushal Kanti; Singh, Pawan Kumar; Geem, Zong Woo; Sarkar, Ram

doi:10.1109/access.2020.2988157

Cited by 52 publications

(38 citation statements)

References 63 publications

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“…This function gives the probability of whether to choose a particular feature in a solution vector. It is a reliable function and has previously been used by many researchers [71,72]. The S-shaped transfer function Figure 4 and shown in Equation 11.…”

Section: A Proposed Ma-hs Algorithmmentioning

confidence: 99%

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

et al. 2020

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Feature selection is a process to reduce the dimension of a dataset by removing redundant features, and to use the optimal subset of features for machine learning or data mining algorithms. This helps to minimize the time requirement to train a learning algorithm as well as to lessen the storage requirement by ignoring the less-informative features. Feature selection can be considered as a combinatorial optimization problem. In this paper, the authors have presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search. Mayfly Algorithm has not hitherto been used for feature selection problems to the best of the author's knowledge. An S-shaped transfer function is incorporated for converting it into a binary version of Mayfly Algorithm. When different candidate solutions obtained from various regions of the search space using Mayfly Algorithm are taken into the harmony memory and processed by Harmony Search, a superior solution can be ensured. This is the primary reason for proposing a hybrid of Mayfly Algorithm and Harmony Search. Thus, combining harmony search with Mayfly Algorithm leads to an increased exploitation of the search space and an overall improvement in the performance of Mayfly-Harmony Search (MA-HS) algorithm. The proposed algorithm has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta-heuristic FS methods. Experiments have also been performed on three high-dimensional microarray datasets. The results obtained support the superior performance of the algorithm compared to the other methods. The source code of the proposed algorithm can be found using the link as follows: https://github.com/trin07/MA-HS.

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Section: A Proposed Ma-hs Algorithmmentioning

confidence: 99%

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

et al. 2020

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“…Recently, meta-heuristic optimization algorithms [21] have become popular amongst researchers. These algorithms have proved their superiority in different fields: medicine, data mining, pattern recognition, finance [22,23,24] etc.…”

Section: Literature Surveymentioning

confidence: 99%

Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

et al. 2020

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Image contrast enhancement is a very important phase for processing of digital images. The main goal of image contrast enhancement is to improve the visual quality by improving the contrast level of images which were distorted or degraded due to casual acquisition of images. The most popular method to perform this task is Histogram Equalization (HE). However, the exhaustive approach taken during HE is an algorithmically complex task. In this paper, we have considered image contrast enhancement as an optimization problem, where a new meta-heuristic algorithm, called Barnacles Mating Optimizer (BMO) is used to find the optimal solution for this optimization problem. A grey level mapping technique is used here to convert an image to a solution of the optimization problem. The algorithm has been evaluated on five publicly available datasets: Kodak, MIT-Adobe FiveK images, H-DIBCO 2016, and H-DIBCO 2018. It is also applied on some standard images like Boy, Lena, Lifting body and Zebra. The obtained results clearly display the effectiveness of the proposed method. The results obtained on the Kodak images are compared with many state-of-the-art methods present in the literature, and the comparison proves the superiority of the proposed method. To test the applicability of BMO in solving real world problems, we have applied it as a pre-processing step in binarization of H

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“…Meanwhile, several heuristic methods that have been used to solve the problems are in the case of exam planning which is complex and time consuming when done manually [16] [17], and A Hybrid Meta-Heuristic Feature Selection Method for Identification of Indian Spoken Languages From Audio Signals [18]. As with previous research, namely Late Acceptance Hill Climbing Based Social Ski Driver Algorithm for Feature Selection [19] and application of the steepest climb method in games puzzle [20] applied the hill climbing method. In this study, we will build an expert engine damage / fault detection system on Marchetti-type airplanes by applying the Heuristic Hill Climbing method.…”

Section: Introductionmentioning

confidence: 99%

Marchetti type of aircraft engine error detection system using hill climbing heuristic

Honggowibowo¹,

Pujiastuti²,

Indrianingsih³

et al. 2021

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Transportation has become a human need today, one of which is air transportation, namely airplanes. In order for the aircraft to be used properly and comfortably, it is necessary to carry out routine maintenance, especially before flying. Problems caused by aircraft engines are sometimes small problems that do not require a high level of knowledge. To overcome this, maybe someone who has very basic knowledge of aircraft engines can do it. But sometimes these problems also require a high level of expertise regarding these components so that they require technicians or experts to repair them. Through this application, users can consult the system such as consulting with experts to find out the symptoms that occur on the plane and find solutions to the problems encountered. The expert system built using the Hill Climbing Heuristic method resulted in 72.67% when tested on respondents.

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Late Acceptance Hill Climbing Based Social Ski Driver Algorithm for Feature Selection

Cited by 52 publications

References 63 publications

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

Marchetti type of aircraft engine error detection system using hill climbing heuristic

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