Joint neighborhood entropy-based gene selection method with fisher score for tumor classification

Chen

et al. 2019

Complexity

Self Cite

Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation

Chen

et al. 2019

Complexity

Self Cite

“…Lyu et al [ 7 ] investigated a filter method based on maximal information coefficient, which eliminates redundant information that does not require additional processes. The wrapper methods use a classifier to find the most discriminant feature subset by minimizing an error prediction function [ 8 ]. Jadhav et al [ 9 ] designed a wrapper feature selection method and performed functional ranking based on information gain directed genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures

Wang

et al. 2019

Entropy

Self Cite

For continuous numerical data sets, neighborhood rough sets-based attribute reduction is an important step for improving classification performance. However, most of the traditional reduction algorithms can only handle finite sets, and yield low accuracy and high cardinality. In this paper, a novel attribute reduction method using Lebesgue and entropy measures in neighborhood rough sets is proposed, which has the ability of dealing with continuous numerical data whilst maintaining the original classification information. First, Fisher score method is employed to eliminate irrelevant attributes to significantly reduce computation complexity for high-dimensional data sets. Then, Lebesgue measure is introduced into neighborhood rough sets to investigate uncertainty measure. In order to analyze the uncertainty and noisy of neighborhood decision systems well, based on Lebesgue and entropy measures, some neighborhood entropy-based uncertainty measures are presented, and by combining algebra view with information view in neighborhood rough sets, a neighborhood roughness joint entropy is developed in neighborhood decision systems. Moreover, some of their properties are derived and the relationships are established, which help to understand the essence of knowledge and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is designed to improve the classification performance of large-scale complex data. The experimental results under an instance and several public data sets show that the proposed method is very effective for selecting the most relevant attributes with high classification accuracy.

“…The selection of empirical risk loss is mostly dependent on the types of noises [ 11 , 12 ]. For example, squared loss is suitable for Gaussian noise [ 13 , 14 , 15 ], least absolute deviation loss for Laplacian noise [ 16 ], and Beta loss for Beta noise [ 17 , 18 , 19 ]. By the formula of the optimization method, a series of optimization algorithms are developed [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…Suykens et al [ 22 , 23 ] constructed least squares SVR with Gaussian noise (LS-SVR). Wu [ 13 ] and Pontil et al [ 24 ] constructed

-SVR with Gaussian noise (GN-SVR). In 2002, Bofinger et al [ 25 ] discovered that the output of a wind turbine system is limited between zero and maximum power and that the error statistics do not follow a normal distribution.…”

Section: Introductionmentioning

confidence: 99%

ν-Support Vector Regression Model Based on Gauss-Laplace Mixture Noise Characteristic for Wind Speed Prediction

Zhang

Zhou

et al. 2019

Entropy

Self Cite

Most regression techniques assume that the noise characteristics are subject to single noise distribution whereas the wind speed prediction is difficult to model by the single noise distribution because the noise of wind speed is complicated due to its intermittency and random fluctuations. Therefore, we will present the ν -support vector regression model of Gauss-Laplace mixture heteroscedastic noise (GLM-SVR) and Gauss-Laplace mixture homoscedastic noise (GLMH-SVR) for complex noise. The augmented Lagrange multiplier method is introduced to solve models GLM-SVR and GLMH-SVR. The proposed model is applied to short-term wind speed forecasting using historical data to predict future wind speed at a certain time. The experimental results show that the proposed technique outperforms the single noise technique and obtains good performance.