Satisfactory feature selection and its application enterprise credit assessment

The selection of evaluating index system is one of the key problems in enterprise credit assessment. It is essentially a satisfactory feature selection (SFS) problem. In this paper, several novel satisfactory-rate functions of feature set (SRFFS) are designed, in which the classification performance of the feature subset and its size are considered compromisingly. The accuracy of SVM Cross Validation is employed as evaluation criterion of classification ability, and the SFS algorithm is described in detail. Contrastive experiments are carried on SFS and three other different feature selection methods: S-SFS, Expert+GAFS and GAFS. Results show that SFS, which can pick out the feature subset with low dimension, high classification accuracy and balanced ranking performance, is superior to three other ones

Credit risk assessment in commercial banks based on fuzzy support vector machines

Credit risk assessment plays an important role in banks credit risk management. The objective of credit assessment is to decide credit ranks, which denote the capacity of enterprises to meet their financial commitments. Traditional "one-versusone" approach has been commonly used in the multi-classification method based on Support Vector Machine (SVM). Since SVM for pattern recognition is based on binary classification, there will be unclassifiable regions when extended to multi-classification problems. Focus on this problem, a new credit risk assessment model based on fuzzy SVM is introduced in this paper that can give a reasonable classification for unclassifiable examples. Experiment results show that the fuzzy SVM method provides a better performance in generalization ability and assessment accuracy than conventional one-versus-one multi-classification approach

Multi-classifier Combination for banks credit risk assessment

Credit risk assessment problem belongs essentially to a classification problem. In this paper, a Multi-classifier Combination algorithm has been developed for banks credit risk assessment. We adopt Back-Propagation (BP) algorithm as the meta-learning algorithm and compared the methods of Bagging and Boosting to construct the Multi-classifier System (MCS). Experimental results on real client's data illustrate the effectiveness of the proposed method

Accuracy of classifier combining based on majority voting

In this paper, we formulate the accuracy of classifier combining which is based on majority voting, there are only two parameter involved, one is the average accuracy of individual classifiers, the other we call it Lapsed Accuracy (LA) is related with the efficiency of classifier combining, and we discuss the theoretical bounds of majority voting via the formula

A strategy of maximizing the sum of weighted margins for ranking multi classification problem

This paper discusses the strategies of maximizing the sum of margins for ranking multi classification problem. First, the strategy of maximizing the sum of margins (MSW is extended to maximizing the sum of weighted margins (MSWM). Using MSWM, a mathematical model is established to deal with the ranking multi classification problems where the importance of margins between classes is different, and its dual model is deduced. Then, by introducing the concept of algebraic margin, which is a generalization of geometric margin, the MSWM is further extended to maximizing the sum of weighed algebraic margins (MSWAM). Based on the MSWAM, the deduced mathematical model of the ranking multi classification problem not only has positive generalization ability, but is also a simple linear programming model

Rank space diversity: A diversity measure of base kernel matrices

This paper studies the diversity measure of base kernel matrices. First, rank space diversity is proposed as a diversity measure of base kernel matrices. Then, a rule for choosing base kernel matrices is deduced by this diversity measure. Last, our rule's validation is claimed by some experiments on artificial data set and benchmark data set

A study on piecewise polynomial smooth approximation to the plus function

In smooth support vector machine (SSVM), the plus function must be approximated by some smooth function, and the approximate error will affect the classification ability. This paper studies the smooth approximation to the plus function by piecewise polynomials. First, some standard piecewise polynomial smooth approximation problems are formulated. Then, the existence and uniqueness of solution for these problems are proved and the analytic solutions are achieved. The comparison between the results in this paper and the previous ones shows that the piecewise polynomial functions in this paper achieve better approximation to the plus function

TCM-RF : Hedging the predictions of Random Forest

The output of traditional classifier is point prediction without giving any confidence of it. To the contrary, Transductive Confidence Machine (TCM), which is a novel framework that provides a prediction result coupled with its accurate confidence. This method also can hedge the prediction in which the predicting accuracy will be controlled by predefined confidence level. In the framework of TCM, the efficiency of prediction depends on the strangeness function of samples. This paper incorporates Random forests (RF) into the framework of TCM and proposes new TCM algorithm named TCM-RF, in which the strangeness obtained by RF will be used to implement the confidence prediction. Compared with traditional TCM algorithms, our method benefits from the more precise and robust strangeness measure and takes advantage of random forest. Experiments indicate its effectiveness and robustness. In addition, our study demonstrated that using ensemble strategies to define sample strangeness may be a more principled way than using a single classifier. On the other hand, it also shows that the paradigm of hedging prediction can be applied to an ensemble classifier

3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images

Wheat is the main food crop today world-wide. In order to improve its yields, researchers are committed to understand the relationships between wheat genotypes and phenotypes. Compared to progressive technology of wheat gene section identification, wheat trait measurement is mostly done manually in a destructive, labor-intensive and time-consuming way. Therefore, this study will be greatly accelerated and promoted if we can automatically discover wheat phenotype in a nondestructive and fast manner. In this paper, we propose a novel pipeline based on 3D morphological processing to detect wheat spike grains and stem nodes from 3D X-ray micro computed tomography (CT) images. We also introduce a set of newly defined 3D phenotypes, including grain aspect ratio, porosity, Grain-to-Grain distance, and grain angle, which are very difficult to be manually measured. The analysis of the associations among these traits would be very helpful for wheat breeding. Experimental results show that our method is able to count grains more accurately than normal human performance. By analyzing the relationships between traits and environment conditions, we find that the Grain-to-Grain distance, aspect ratio and porosity are more likely affected by the genome than environment (only tested temperature and water conditions). We also find that close grains will inhibit grain volume growth and that the aspect ratio 3.5 may be the best for higher yield in wheat breeding

A comparison of multi-classification methods for credit risk assessment in commercial banks

Credit Risk Assessment problem belongs essentially to an ordinal classification problem. How to construct an available assessing model is an important part in commercial banks credit management. In this paper, several multi-classification strategies, such as "One-against-rest", "One-against-one", "Directed Acyclic Graph", and "Embedded Space", are compared and applied them to construct credit risk assessment models respectively. By experiments, their assessing performances are compared and DAGSVM and ESSVM are more suitable for our purpose