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Imbalanced data classification is a major challenge in the field of data mining and machine learning, and oversampling algorithms are a widespread technique for re-sampling imbalanced data. To address the problems that existing oversampling methods tend to introduce noise points and generate overlapping instances, in this paper, we propose a novel oversampling method based on density peaks clustering. Firstly, density peaks clustering algorithm is used to cluster minority instances while screening outlier points. Secondly, sampling weights are assigned according to the size of clustered sub-clusters, and new instances are synthesized by interpolating between cluster cores and other instances of the same sub-cluster. Finally, comparative experiments are conducted on both the artificial data and KEEL datasets. The experiments validate the feasibility and effectiveness of the algorithm and improve the classification accuracy of the imbalanced data.

Species identification through DNA barcoding or metabarcoding has become a key approach for biodiversity evaluation and ecological studies. However, the rapid accumulation of barcoding data has created some difficulties: for instance, global enquiries to a large reference library can take a very long time. We here devise a two-step searching strategy to speed identification procedures of such queries. This firstly uses a Hidden Markov Model (HMM) algorithm to narrow the searching scope to genus level and then determines the corresponding species using minimum genetic distance. Moreover, using a fuzzy membership function, our approach also estimates the credibility of assignment results for each query. To perform this task, we developed a new software pipeline, FuzzyID2, using Python and C++. Performance of the new method was assessed using eight empirical data sets ranging from 70 to 234,535 barcodes. Five data sets (four animal, one plant) deployed the conventional barcode approach, one used metabarcodes, and two were eDNA-based. The results showed mean accuracies of generic and species identification of 98.60% (with a minimum of 95.00% and a maximum of 100.00%) and 94.17% (with a range of 84.40%-100.00%), respectively. Tests with simulated NGS sequences based on realistic eDNA and metabarcode data demonstrated that FuzzyID2 achieved a significantly higher identification success rate than the commonly used Blast method, and the TIPP method tends to find many fewer species than either FuzztID2 or Blast. Furthermore, data sets with tens of thousands of barcodes need only a few seconds for each query assignment using FuzzyID2. Our approach provides an efficient and accurate species identification protocol for biodiversity-related projects with large DNA sequence data sets.

We propose a novel nonparallel classifier, called nonparallel support vector machine (NPSVM), for binary classification. Our NPSVM that is fully different from the existing nonparallel classifiers, such as the generalized eigenvalue proximal support vector machine (GEPSVM) and the twin support vector machine (TWSVM), has several incomparable advantages: 1) two primal problems are constructed implementing the structural risk minimization principle; 2) the dual problems of these two primal problems have the same advantages as that of the standard SVMs, so that the kernel trick can be applied directly, while existing TWSVMs have to construct another two primal problems for nonlinear cases based on the approximate kernel-generated surfaces, furthermore, their nonlinear problems cannot degenerate to the linear case even the linear kernel is used; 3) the dual problems have the same elegant formulation with that of standard SVMs and can certainly be solved efficiently by sequential minimization optimization algorithm, while existing GEPSVM or TWSVMs are not suitable for large scale problems; 4) it has the inherent sparseness as standard SVMs; 5) existing TWSVMs are only the special cases of the NPSVM when the parameters of which are appropriately chosen. Experimental results on lots of datasets show the effectiveness of our method in both sparseness and classification accuracy, and therefore, confirm the above conclusion further. In some sense, our NPSVM is a new starting point of nonparallel classifiers.

Regularized multiple-criteria linear programming (RMCLP) model is a new powerful method for classification and has been used in various real-life data mining problems. In this paper, instead of solving quadratic programming (QP) in algorithm RMCLP, we proposed a novel RMCLP via linear programming (LP) for pattern classification(called LP-RMCLP). Numerical experiments on benchmark datasets show that the proposed method is comparable to traditional RMCLP in accuracy, however considerably faster than it.

Classification is a main data mining task, which aims at predicting the class label of new input data on the basis of a set of pre-classified samples. Multiple criteria linear programming (MCLP) is used as a classification method in the data mining area, which can separate two or more classes by finding a discriminate hyperplane. Although MCLP shows good performance in dealing with linear separable data, it is no longer applicable when facing with nonlinear separable problems. A kernel-based multiple criteria linear programming (KMCLP) model is developed to solve nonlinear separable problems. In this method, a kernel function is introduced to project the data into a higher-dimensional space in which the data will have more chance to be linear separable. KMCLP performs well in some real applications. However, just as other prevalent data mining classifiers, MCLP and KMCLP learn only from training examples. In the traditional machine learning area, there are also classification tasks in which data sets are classified only by prior knowledge, i.e. expert systems. Some works combine the above two classification principles to overcome the faults of each approach. In this paper, we provide our recent works which combine the prior knowledge and the MCLP or KMCLP model to solve the problem when the input consists of not only training examples, but also prior knowledge. Specifically, how to deal with linear and nonlinear knowledge in MCLP and KMCLP models is the main concern of this paper. Numerical tests on the above models indicate that these models are effective in classifying data with prior knowledge.

The Universum, which is defined as the sample not belonging to either class of the classification problem of interest, has been proved to be helpful in supervised learning. In this work, we designed a new Twin Support Vector Machine with Universum (called U-TSVM), which can utilize Universum data to improve the classification performance of TSVM. Unlike U-SVM, in U-TSVM, Universum data are located in a nonparallel insensitive loss tube by using two Hinge Loss functions, which can exploit these prior knowledge embedded in Universum data more flexible. Empirical experiments demonstrate that U-TSVM can directly improve the classification accuracy of standard TSVM that use the labeled data alone and is superior to U-SVM in most cases.

In this paper, a classification method named nearest subspace method is applied for credit risk evaluation. Virtually credit risk evaluation is a very typical classification problem to identify “good” and “bad” creditors. Currently some machine learning technologies, such as support vector machine (SVM), have been discussed widely in credit risk evaluation. But there are many effective classification methods in pattern recognition and artificial intelligence have not been tested for credit evaluation. This paper presents to use nearest subspace classification method, a successful face recognition method, for credit evaluation. The nearest subspace credit evaluation method use the subspaces spanned by the creditors in same class to extend the training set, and the Euclidean distance from a test creditor to the subspace is taken as the similarity measure for classification, then the test creditor belongs to the class of nearest subspace. Experiments on real world credit dataset show that the nearest subspace credit risk evaluation method is a competitive method.

Intrusion detection system (IDS) is an inseparable part of each computer networks to monitor the events and attacks, which many researchers proposed variety of models to improve the performance of IDS. In this paper we present a new method based on multiple criteria linear programming and particle swarm optimization to enhance the accuracy of attacks detection. Multiple criteria linear programming is a classification method based on mathematical programming which has been showed a potential ability to solve real-life data mining problems. However, tuning its parameters is an essential steps in training phase. Particle swarm optimization (PSO) is a robust and simple to implement optimization technique has been used in order to improve the performance of MCLP classifier. KDD CUP 99 dataset used to evaluate the performance of proposed method. The result demonstrated the proposed model has comparable performance based on detection rate, false alarm rate and running time compare to two other benchmark classifiers.