Abstract
By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research thinking concerning extreme learning machine into the economics classification area so as to fulfill the purpose of computerizing the speedy but effective evaluation of massive financial statements of listed companies pertain to different classes
Highlights
The financial statements of listed companies constitute an important source of information from which they can understand the current financial status of those listed companies and reasonably predict their development tendency
There are several classification algorithms having been precedentedly proposed, for example, the Five-Class Determination Method suggested by Lv Changjiang[2] and others, which suggests to group the financial situation of listed companies into the following 5 classes: financial idleness, financial adequacy, financial balance, financial difficulty and financial bankruptcy, the Support Vector Machine (SVM) Classification Algorithm recommended by Song Jiao[3] that establishes a SVMbased financial crisis pre-warning model for listed companies to effectively prevent financial crisis, and the BP Neural Network Pre-warning Model brought forth by Yang Shu’e[4] and others, which aims to help listed companies evade financial crisis
5 Comparison between Extreme Learning Machine Classification Algorithm Basedupon DE Optimization and Traditional Extreme Learning Machine Classification Algorithm. We divided those 200 listed companies into the following 2 parts, one is made up of 140 companies that have been respectively imported into the traditional extreme learning machine and the extreme learning machine based upon DE optimization in the Matlab environment, and the other is the remaining 60 companies whose financial statements are utilized as test samples to observe the classification situation
Summary
The financial statements of listed companies constitute an important source of information from which they can understand the current financial status of those listed companies and reasonably predict their development tendency. The purpose of this article is to put forth a model of extreme learning machine based upon DE optimization that is capable of modeling the financial status of a listed company after learning and analyzing its financial statement samples in a given quantity and classifying the enormous financial data of other listed companies imported later on so as to save the time spent on classification and optimize the classification effect. As for its algorithm thinking, please refer to as follows: For a single-hidden layer neural network, assume there are N random samples ( Xi , ti ) , wherein: Xi [xi , xi2 ,..., xin ]T Rn , ti [ti , ti2 ,..., tim ]T Rm DŽ (4). The purpose the single-hidden neural network learning intends to serve is to minimize the tolerance of output, which can be expressed in Formula (6):. Moore-Penrose generalized inverse of the matrix H and the solution Eproves to be mere and minimum
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