A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology

Hosseinalipour, Ali; Gharehchopogh, Farhad Soleimanian; Màsdàrí, Mohàmmàd; Khademi, Ali

doi:10.1007/s10489-020-02038-y

Cited by 24 publications

(11 citation statements)

References 47 publications

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“…This section shows a comparison between IBSCA3 and other new nature-inspired metaheuristic algorithms, including: A novel Binary Farmland Fertility Algorithm (BFFAG) [ 122 ], African vultures optimization algorithm (AVOA) [ 123 ] and Artificial gorilla troops optimizer (GTO) [ 124 ]. Table 13 shows the parameter settings of these algorithms, as in [ 122 – 124 ].…”

Section: Methodsmentioning

confidence: 99%

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

et al. 2022

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This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features to best represent a dataset. SCA is a recent metaheuristic algorithm established to emulate a model based on sine and cosine trigonometric functions. It was initially proposed to tackle problems in the continuous domain. The SCA has been modified to Binary SCA (BSCA) to deal with the binary domain of the FS problem. To improve the performance of BSCA, three accumulative improved variations are proposed (i.e., IBSCA1, IBSCA2, and IBSCA3) where the last version has the best performance. IBSCA1 employs Opposition Based Learning (OBL) to help ensure a diverse population of candidate solutions. IBSCA2 improves IBSCA1 by adding Variable Neighborhood Search (VNS) and Laplace distribution to support several mutation methods. IBSCA3 improves IBSCA2 by optimizing the best candidate solution using Refraction Learning (RL), a novel OBL approach based on light refraction. For performance evaluation, 19 real-wold datasets, including a COVID-19 dataset, were selected with different numbers of features, classes, and instances. Three performance measurements have been used to test the IBSCA versions: classification accuracy, number of features, and fitness values. Furthermore, the performance of the last variation of IBSCA3 is compared against 28 existing popular algorithms. Interestingly, IBCSA3 outperformed almost all comparative methods in terms of classification accuracy and fitness values. At the same time, it was ranked 15 out of 19 in terms of number of features. The overall simulation and statistical results indicate that IBSCA3 performs better than the other algorithms.

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Section: Methodsmentioning

confidence: 99%

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

et al. 2022

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“…The neural network-based methods use the attention mechanism to assign different semantic weights to words with good experimental results in many downstream tasks, such as LSTM [6], BiLSTM [7] and BERT [8]. Semantic analysis based on attention mechanism has been involved in many works [9][10][11] and can reflect the different weights of words in different texts. The attention mechanism is introduced to obtain different weight of words in order to extract enough key information.…”

Section: Attention-based Semantic Analysismentioning

confidence: 99%

A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis

Qiu

Yan

2022

Mathematics

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Text representation is an important topic in the field of natural language processing, which can effectively transfer knowledge to downstream tasks. To extract effective semantic information from text with unsupervised methods, this paper proposes a quantum language-inspired tree structural text representation model to study the correlations between words with variable distance for semantic analysis. Combining the different semantic contributions of associated words in different syntax trees, a syntax tree-based attention mechanism is established to highlight the semantic contributions of non-adjacent associated words and weaken the semantic weight of adjacent non-associated words. Moreover, the tree-based attention mechanism includes not only the overall information of entangled words in the dictionary but also the local grammatical structure of word combinations in different sentences. Experimental results on semantic textual similarity tasks show that the proposed method obtains significant performances over the state-of-the-art sentence embeddings.

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“…For example, the chaotic election algorithm (CEA) [12] embeds the chaos-based advertisement operator to the conventional PEA algorithm [11] to improve its search capability and convergence speed. Some other algorithms that recently proposed and used in different applications are opposition-based learning firefly algorithm combined with dragonfly algorithm (OFADA) [69], random memory and elite memory equipped artificial bee colony (ABCWOA) algorithm [70], efficient binary symbiotic organisms search (EBSOS) [71,72], efficient binary chaotic symbiotic organisms search (EBCSOS) [73], and binary farmland fertility algorithm (BFFA) [74].…”

Section: Evolutionary Swarm Intelligencementioning

confidence: 99%

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

Emami

2021

J Supercomput

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In this paper, a human-inspired optimization algorithm called stock exchange trading optimization (SETO) for solving numerical and engineering problems is introduced. The inspiration source of this optimizer is the behavior of traders and stock price changes in the stock market. Traders use various fundamental and technical analysis methods to gain maximum profit. SETO mathematically models the technical trading strategy of traders to perform optimization. It contains three main actuators including rising, falling, and exchange. These operators navigate the search agents toward the global optimum. The proposed algorithm is compared with seven popular meta-heuristic optimizers on forty single-objective unconstraint numerical functions and four engineering design problems. The statistical results obtained on test problems show that SETO is capable of providing competitive and promising performances compared with counterpart algorithms in solving optimization problems of different dimensions, especially 1000-dimension problems. Out of 40 numerical functions, the SETO algorithm has achieved the global optimum on 36 functions, and out of 4 engineering problems, it has obtained the best results on 3 problems.

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A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology

Cited by 24 publications

References 47 publications

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

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