International Journal of Pattern Recognition and Artificial Intelligence

Due to the radical change in both Chinese and global economic environment, it is essential to develop a practical model to predict financial distress. The support vector machine (SVM), a new outstanding learning machine based on the statistical learning theory, embodying the principle of structural risk minimization instead of empirical risk minimization principle, is a promising method for such financial distress prediction. However, to some extent, the performance of single classifier depends on the sample's pattern characteristics and each single classifier has its own uncertainty. Using the ensemble methods to predict financial distress becomes a rising trend in this field. This research puts forward a SVM ensemble based on the Choquet integral for financial distress prediction in which Bagging algorithm is used to generate new training sets. The proposed ensemble method can be expressed as "Choquet + Bagging + SVMs". With real data from Chinese listed companies, an experiment is carried out to compare the performance of single classifiers with the proposed ensemble method. Empirical results indicate that the proposed ensemble of SVMs based on the Choquet integral for financial distress prediction has higher average accuracy and stability than single SVM classifiers.

We propose an effective outlier detection algorithm for high-dimensional data. We consider manifold models of data as is typically assumed in dimensionality reduction/manifold learning. Namely, we consider a noisy data set sampled from a low-dimensional manifold in a high-dimensional data space. Our algorithm uses local geometric structure to determine inliers, from which the outliers are identified. The algorithm is applicable to both linear and nonlinear models of data. We also discuss various implementation issues and we present several examples to demonstrate the effectiveness of the new approach.

We relax the equality constraints in the very general and well-known affine Schatten p-norm minimization problem into complete loss function-based constraints. Owing to the imposed equality constraints, existing methods only have limited degree of model flexibility, via their optimization of the objective energy function. By our proposed transformation, the decision variables in the objective function can directly achieve L0 norm minimization via the process of enumerating the matrix rank (i.e. matrix ordering constraint). We show that, our new objective function is still reasonable, and its minimum can be obtained by a more general form of the Fixed-Point Continuation framework with almost the same computational cost at each matrix order enumeration. Experiments show that, our algorithm has good performance compared to its predecessor over some datasets and applications.

In many real-world applications, it is often the case that the class distribution of instances is imbalanced and the costs of misclassification are different. Thus, the class-imbalanced cost-sensitive learning has attracted much attention from researchers. Sampling is one of the widely used techniques in dealing with the class-imbalance problem, which alters the class distribution of instances so that the minority class is well represented in the training data. In this paper, we propose a novel Minority Cloning Technique (MCT) for class-imbalanced cost-sensitive learning. MCT alters the class distribution of training data by cloning each minority class instance according to the similarity between it and the mode of the minority class. The experimental results on a large number of UCI datasets show that MCT performs much better than Minority Oversampling with Replacement Technique (MORT) and Synthetic Minority Oversampling TEchnique (SMOTE) in terms of the total misclassification costs of the built classifiers.

It is well known that the performance of kernel methods depends on the choice of appropriate kernels and associated parameters. While cross-validation (CV) is a useful method of kernel and parameter choice for supervised learning such as the support vector machines, there are no general well-founded methods for unsupervised kernel methods. This paper discusses CV for kernel canonical correlation analysis (KCCA), and proposes a new regularization approach for KCCA. As we demonstrate with Gaussian kernels, the CV errors for KCCA tend to decrease as the bandwidth parameter of the kernel decreases, which provides inappropriate features with all the data concentrated in a few points. This is caused by the ill-posedness of the KCCA with the CV. To solve this problem, we propose to use constraints on the fourth-order moments of canonical variables in addition to the variances. Experiments on synthesized and real-world data demonstrate that the proposed higher-order regularized KCCA can be applied effectively with the CV to find appropriate kernel and regularization parameters.

This paper proposes a high-capacity reversible watermarking algorithm for medical image analysis based on the difference block histogram, which will benefit the medical image authentication and doctor–patient confidentiality. By dividing the original medical image into blocks, the method displaces the peak point of a block's histogram of difference and embeds multi-bit information at 1 pixel point. In so doing, secret communication and storage of large-capacity invisible medical diagnoses and patients personal confidential data can be achieved. Once the watermark is extracted, not only the image integrity is authenticated, but also the original image and personal data of the patient can be recovered in a nondestructive way. With low computational complexity, a high embedding capacity and little demand for auxiliary information, the proposed algorithm is highly secure and practical.

The driver-assistance system (DAS) becomes quite necessary in-vehicle equipment nowadays due to the large number of road traffic accidents worldwide. An efficient DAS detecting hazardous situations robustly is key to reduce road accidents. The core of a DAS is to identify salient regions or regions of interest relevant to visual attended objects in real visual scenes for further process. In order to achieve this goal, we present a method to locate regions of interest automatically based on a novel adaptive mean shift segmentation algorithm to obtain saliency objects. In the proposed mean shift algorithm, we use adaptive Bayesian bandwidth to find the convergence of all data points by iterations and the k-nearest neighborhood queries. Experiments showed that the proposed algorithm is efficient, and yields better visual salient regions comparing with ground-truth benchmark. The proposed algorithm continuously outperformed other known visual saliency methods, generated higher precision and better recall rates, when challenged with natural scenes collected locally and one of the largest publicly available data sets. The proposed algorithm can also be extended naturally to detect moving vehicles in dynamic scenes once integrated with top-down shape biased cues, as demonstrated in our experiments.

This paper presents an algorithm for reading digital video clocks reliably and quickly. Reading digital clocks from videos is difficult due to the challenges such as color variety, font diversity, noise, and low resolution. The proposed algorithm overcomes these challenges by using the novel methods derived from the domain knowledge. This algorithm first localizes the digits of a digital video clock and then recognizes the digits representing the time of digital video clock. It is a robust three-step algorithm. The first step is an efficient procedure that directly identifies the region of the second digit at a very low computational cost, which replaces the traditional tedious image processing procedure of identifying the second digit region. The success of the first step mainly leverages on the novel second-pixel periodicity method. Using the acquired second digit region as input, the second step is a clock digit localization procedure. It first acquires the colors of the digits of the digital video clock and performs the color conversion. Then it localizes the remaining clock digits. Finally, the last step is a clock digit recognition procedure. It first employs an enhanced digit-sequence recognition method to robustly recognize the digits on the second; it then adopts a deep learning procedure to recognize the remaining digits. The proposed algorithm is tested on a prepared benchmark of 1000 videos that is publicly available and the experimental results show that it can read digital video clocks with a 100% accuracy at a low computational cost.

Finger vein pattern has become one of the most promising biometric identifiers. In this paper, a robust method based on Bag-of-Words (BoW) is developed for finger vein verification. Firstly, some robust and discriminative visual words are learned from local base features such as Local Binary Pattern (LBP), Mean Curvature and Webber Local Descriptor (WLD). We name these visual words as Finger Vein Textons (FVTs). Secondly, each image is mapped into a FVTs matrix. Finally, spatial pyramid matching (SPM) method is applied to maintain spatial layout information by representing each image as pyramid histogram which is performed for matching by histogram intersection function. Experimental results show that the proposed method achieves satisfactory performance both on our database and the open PolyU database. In addition, our method also has strong robustness and high accuracy on the self-built rotation and illumination databases.

Representation-based classification have received much attention in the field of face recognition. Collaborative representation-based classification (CRC) has shown the robustness and high performance. In this paper, we proposed a new feature extraction method-based collaborative representation. Firstly, we get the coefficients of all face samples by collaborative representation. Then we define the inter-class reconstructive errors and intra-class reconstructive errors for each sample. After that, Fisher criterion is used to get the discriminative feature. At last, CRC is executed to get the identification results in the new feature space. Different from other feature extraction methods, the proposed method integrates the classification criterion into the feature extraction. So the feature space we get fits the classifier better. Experiment results on several face databases show that the proposed method is more effective than other state-of-the-art face recognition methods.

Invasive weed optimization (IWO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm are inclined to fall into local optimum with lower convergence accuracy when separately used to deal with large scale global optimization (LSGO) problems. In order to fully utilize the advantages of these two intelligent algorithms and complement each other, following the idea of portfolio optimization, this paper correspondingly adjusts and improves the quantum models of IWO and QPSO, organically integrates the two algorithms, and proposes the quantum-behaved invasive weed optimization (QIWO) algorithm. This mixed algorithm can achieve the purpose of information exchange and cooperative search through alternate search enables the make algorithm converge to the optimal solution quickly, properly overcoming the defects of falling into local optimum and premature convergence. Test results of 20 LSGO functions show that compared with other algorithms, QIWO has stronger global optimization capability, faster convergence speed and higher convergence accuracy.

How to solve constrained optimization problems (COPs) is a significant research issue and we combine the bat-inspired algorithm (BA) with differential evolution (DE) into a new hybrid algorithm called BA-DE for solving the COPs. Traditional BAs are prone to sink into stagnation or local optima when no bat individual founds a better location than the past locations for several generations. DE is adopted for updating the past location of bat individuals to force BA to jump out of stagnation or local optima, since it has a great local searching capability. The performance of BA-DE algorithm is improved by the proposed hybrid mechanism. We use 24 well-known benchmark functions to verify the overall performance of our proposed algorithm. Comparisons show that BA-DE outperforms most advanced methods in terms of the final solution's quality.