Using the genetic algorithm to enhance nonnegative matrix factorization initialization

Nonnegative matrix factorization (NMF) algorithms have been utilized in a wide range of real applications; however, the performance of NMF is highly dependent on three factors including: (1) choosing a problem dependent cost function; (2) using an effective initialization method to start the updating procedure from a near‐optimal point; and (3) determining the rank of factorized matrices prior to decomposition. Due to the nonconvex nature of the NMF cost function, finding an analytical‐based optimal solution is impossible. This paper is aimed at proposing an efficient initialization method to modify the NMF performance. To widely explore the search space for initializing the factorized matrices in NMF, the island genetic algorithm (IGA) is employed as a diverse multiagent search scheme. To adapt IGA for NMF initialization, we present a specific mutation operator. To assess how the proposed IGA initialization method efficiently enhances NMF performance, we have implemented state‐of‐the‐art initialization methods and applied to the Japanese Female Facial Expression dataset to recognize the facial expression states. Experimental results demonstrate the superiority of the proposed approach to the compared methods in terms of relative error and fast convergence.     

The mass appraisal of the real estate by computational intelligence

Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches – support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center.     

Using genetic algorithm based knowledge refinement model for dividend policy forecasting

Dividend policy is one of most important managerial decisions affecting the firm value. Although there are many studies regarding decision-making problems, such as credit policy decisions through bankruptcy prediction and credit scoring, there is no research, to our knowledge, about dividend prediction or dividend policy forecasting using machine learning approaches in spite of the significance of dividends. For dealing with the problems involved in literature, we suggest a knowledge refinement model that can refine the multiple rules extracted through rule-based algorithms from dividend data sets by utilizing genetic algorithm (GA). The new technique, called “GAKR (genetic algorithm knowledge refinement)”, aims to combine the advantages of both knowledge consolidation and GA. The main result of the cross-validation procedure is the average accuracy rate of prediction in the five sets over the five iterations. The experiments show that GAKR model always outperforms other models in the performance of dividend policy prediction; we can predict future dividend policy more correctly than any other models. The major advantages of GAKR model can be summarized as follows: (1) Classification process of GAKR can be very fast with a compact set of rules. In other words, fast training mechanism of GAKR is possible regardless of data set sizes. (2) Multiple rules extracted by GAKR development process are much simpler and easier to understand. Moreover, GAKR model can discriminate redundant rules and inconsistent rules.     

Supervised feature selection algorithm via discriminative ridge regression

This paper studies a new feature selection method for data classification that efficiently combines the discriminative capability of features with the ridge regression model. It first sets up the global structure of training data with the linear discriminant analysis that assists in identifying the discriminative features. And then, the ridge regression model is employed to assess the feature representation and the discrimination information, so as to obtain the representative coefficient matrix. The importance of features can be calculated with this representative coefficient matrix. Finally, the new subset of selected features is applied to a linear Support Vector Machine for data classification. To validate the efficiency, sets of experiments are conducted with twenty benchmark datasets. The experimental results show that the proposed approach performs much better than the state-of-the-art feature selection algorithms in terms of the evaluating indicator of classification. And the proposed feature selection algorithm possesses a competitive performance compared with existing feature selection algorithms with regard to the computational cost.     

An affine subspace clustering algorithm based on ridge regression

Recent subspace clustering algorithms, which use sparse or low-rank representations, conduct clustering by considering the errors and noises into their objective functions. Then, the similarity matrix is solved via alternating direction method of multipliers. However, these approaches are subject to the restriction that the characteristic of errors and outliers in sample points should be known as the prior information. Furthermore, these algorithms are time-consuming during the iterative process. Motivated by this observation, this paper proposes a new subspace clustering algorithm: an affine subspace clustering algorithm based on ridge regression. The method introduces ridge regression as objective function which applies affine criteria into subspace clustering. An analytic solution to the problem has been determined for the coefficient matrix. Experimental results obtained on face datasets demonstrate that the proposed method not only improves the accuracy of the clustering results, but also enhances the robustness. Furthermore, the proposed method reduces the computational complexity.     

一种改进的实数编码混合遗传算法

为解决简单遗传算法的不成熟收敛和收敛速度慢的问题,针对实数编码遗传算法提出了初始种群的网格分布法,单步遗传操作后的最优个体保留策略,以及改进的动态交叉和自适应变异概率等,并应用上代最优个体替换当代最差个体的种群进化方法和近亲交叉回避机制等措施对其进行了综合改进。算例表明,该改进算法能有效实现全局优化,提高进化效率,对求解复杂的优化问题具有广泛的适应性。     

Pruning boosted classifiers with a real valued genetic algorithm

Ensemble classifiers and algorithms for learning ensembles have recently received a great deal of attention in the machine learning literature (R.E. Schapire, Machine Learning 5(2) (1990) 197–227;N. Cesa-Bianchi, Y. Freund, D. Haussler, D.P. Helbold, R.E. Schapire, M.K. Warmuth, Proceedings of the 25th Annual ACM Symposium on the Theory of Computing, 1993, pp. 382–391; L. Breiman, Bias, Technical Report 460, Statistics Department, University of California, Berkeley, CA, 1996; J.R. Quinlan, Proceedings of the 14th International Conference on Machine Learning, Italy, 1997; Y. Freund, R.E. Schapire, Proceedings of the 13th International Conference on Machine Learning ICML96, Bari, Italy 1996, pp. 148–157; A.J.C. Sharkey, N.E. Sharkey, Combining diverse neural nets, The Knowledge Engineering Review 12 (3) (1997) 231–247). In particular, boosting has received a great deal of attention as a mechanism by which an ensemble of classifiers that has a better generalisation characteristic than any single classifier derived using a particular technique can be discovered. In this article, we examine and compare a number of techniques for pruning a classifier ensemble which is overfit on its training set and find that a real valued GA is at least as good as the best heuristic search algorithm for choosing an ensemble weighting.     

利用聚类算法提高基于内容的图像检索准确率

给出了一种基于内容的图像检索算法,该算法使用了图像的颜色直方图作为图像的检索特征,并且利用了K均值聚类算法以及用户相关反馈技术来提高检索的准确率。     

Hybrid real coded genetic algorithm solution to economic dispatch problem

This paper presents a new, two-phase hybrid real coded genetic algorithm (GA) based technique to solve economic dispatch (ED) problem with multiple fuel options. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local optimization by direct search and systematic reduction in size of the search region method is next employed to do the fine tuning. Constraint satisfaction technique has been employed to improve the solution quality and reduce the computational expenses. In order to validate the effectiveness of the proposed hybrid real coded genetic algorithm, the result of 10-generation unit ED problem with multiple fuel options is considered. The result shows that the proposed hybrid algorithm not only improves the solution accuracy and reliability but also makes the algorithm more efficient in terms of number of function evaluations and computation time. The simulation study clearly demonstrates that the proposed hybrid real coded genetic algorithm is practical and valid for real-time applications.     

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).     

An approximation to solve regression problems with a genetic fuzzy rule ordinal algorithm

Regression problems try estimating a continuous variable from a number of characteristics or predictors. Several proposals have been made for regression models based on the use of fuzzy rules; however, all these proposals make use of rule models in which the irrelevance of the input variables in relation to the variable to be approximated is not taken into account. Regression problems share with the ordinal classification the existence of an explicit relationship of order between the values of the variable to be predicted. In a recent paper, the authors have proposed an ordinal classification algorithm that takes into account the detection of the irrelevance of input variables. This algorithm extracts a set of fuzzy rules from an example set, using as the basic model a sequential covering strategy along with a genetic algorithm. In this paper, a proposal for a regression algorithm based on this ordinal classification algorithm is presented. The proposed model can be interpreted as a multiclassifier and multilevel system that learns at each stage using the knowledge gained in previous stages. Due to similarities between regression and ordinal problems as well as the use of a set of ordinal algorithms, an error interval can be returned with the regression output value. Experimental results show the good behavior of the proposal as well as the results of the error interval.     

Attention-based multi-modal fusion for improved real estate appraisal: a case study in Los Angeles

Multimedia Tools and Applications - The geographical presentation of a house, which refers to the sightseeing and topography near the house, is a critical factor to a house buyer. The street map is...     

基于稀疏编码和岭回归的文本图像复原算法

为解决现有稀疏编码方法在文本图像复原中存在的编码码元表述空间有限和计算时间长的问题,提出了一种基于岭回归的稀疏编码文本图像复原方法。首先,该方法在训练阶段使用样本图像块训练出用于稀疏表达的字典,并根据样本图像块和编码码元之间的欧氏距离对样本图像块进行聚类;其次,在局部流形空间构建低质量文本图像块和清晰文本图像块之间的岭回归,实现对编码码元表述空间的局部多线性扩展和快速计算;最后,在测试阶段搜索和低质量文本图像最相近的编码码元,计算出近似的清晰文本图像块,从而避免计算耗时的低质量文本图像块的稀疏编码。实验结果表明,所提算法在恢复的图像质量上相比现有的基于稀疏编码的算法在峰值信噪比上高0.3~1.1 dB,耗时降低了1~2个数量级,为提高文本图像复原质量和提升算法运算速度提供了一种解决方案。     

实码加速遗传算法在改进TOPSIS法中的应用

为改进TOPSIS法,分别以方案点靠近理想点和远离负理想点为目标,构建非线性规划模型。运用实码加速遗传算法（RAGA）进行求解,可较方便地获得兼具决策方法适应性和决策者偏好的指标综合权重。由此,基于RAGA的改进TOPSIS法可在一定程度上克服传统TOPSIS法的不足。应用实例证明了该方法的可行性和有效性。     

A simulation approach to real estate investment analysis

This paper describes a Monte Carlo simulation approach to the analysis of real estate investments under uncertainty. The procedure suggested in the paper is based upon the use of computer simulation to provide realistic estimates of the internal rate of return on a real estate investment and the risk of that investment. The mathematical structure and development of the computerized real estate investment analysis model are described in detail, and computational results from the model's application are presented and discussed.     

优化岭参数的非线性岭回归及4-CBA含量软测量

针对二甲苯氧化反应过程中影响主要副产物对羧基苯甲醛含量的因素众多且呈高度非线性的问题,提出基于优化岭参数的非线性岭回归MNRR算法,并应用于建立4 CBA含量软测量模型,获得满意的结果.MNRR采用非线性变换对原始模式特征空间进行扩张,以预测性能为指标,采用进化算法确定最佳岭参数,最终建立具有强非线性表达能力以及预测性能良好的模型.与非线性最小二乘回归和基于广义交叉有效性逐步估计岭参数的非线性岭回归相比,MNRR模型具有更高的预测精度且克服了传统岭回归算法最佳岭参数难以确定的缺点.     

An improved ridge regression algorithm and its application in predicting TV ratings

Ridge regression is a biased estimated regressive method, which is traditionally used in collinearity data analysis. It is actually a modified Least Square method, which gains more rational and reliable regression coefficient by giving up the unbiasedness of Least Squares Estimation, reducing partial information and decreasing accuracy to overcome the over-fitting problems. This article presents an improved ridge regression algorithm and utilizes it to predict the audience rating for TV ratings. It is tested by 10 - fold Cross Validation. TV rating is an important indication to measure the quality and user experience, as well as one of the vital standards to state the value of a TV channel. The improved ridge regression algorithm is used to learn the model of weight matrix, which is trained by the error algorithm to predict the TV ratings. The extensive experimental results demonstrate the effectiveness of the proposed algorithm.

     

基于遗传退火三次指数平滑的期货预测研究

为更准确地对期货价格进行预测分析,提出了一种对三次指数平滑法的平滑初值和平滑参数值进行自适应选取的预测方法.该方法根据样本预测误差平方和最小来自适应地调整平滑参数与平滑初值,以避免对其进行主观选取,并将此方法应用于期货价格的预测中.实验结果表明,该方法与单独的三次指数平滑法相比,对于期货价格预测的准确率有所提高,可以得到比较有效的预测值.     

A modified genetic algorithm for forecasting fuzzy time series

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.     

Implementation of real estate contract system using zero knowledge proof algorithm based blockchain

The Journal of Supercomputing - A real estate contract is a high-risk contract with a large amount of money, and there are many problems in the risk and reliability of fraud. In particular, online...