Haiqin Yang

Support Vector Machine Regression for Volatile Stock Market Prediction

Recently, Support Vector Regression (SVR) has been introduced to solve regression and prediction problems. In this paper, we apply SVR to financial prediction tasks. In particular, the financial data are usually noisy and the associated risk is time-varying. Therefore, our SVR model is an extension of the standard SVR which incorporates margins adaptation. By varying the margins of the SVR, we could reflect the change in volatility of the financial data. Furthermore, we have analyzed the effect of asymmetrical margins so as to allow for the reduction of the downside risk. Our experimental results show that the use of standard deviation to calculate a variable margin gives a good predictive result in the prediction of Hang Seng Index.

Learning classifiers from imbalanced data based on biased minimax probability machine

We consider the problem of the binary classification on imbalanced data, in which nearly all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. Traditional machine learning methods seeking an accurate performance over a full range of instances are not suitable to deal with this problem, since they tend to classify all the data into the majority, usually the less important class. Moreover, some current methods have tried to utilize some intermediate factors, e.g., the distribution of the training set, the decision thresholds or the cost matrices, to influence the bias of the classification. However, it remains uncertain whether these methods can improve the performance in a systematic way. In this paper, we propose a novel model named biased minimax probability machine. Different from previous methods, this model directly controls the worst-case real accuracy of classification of the future data to build up biased classifier;. Hence, it provides a rigorous treatment on imbalanced data. The experimental results on the novel model comparing with those of three competitive methods, i.e., the naive Bayesian classifier, the k-nearest neighbor method, and the decision tree method C4.5, demonstrate the superiority of our novel model.

Efficient Sparse Generalized Multiple Kernel Learning

Kernel methods have been successfully applied in various applications. To succeed in these applications, it is crucial to learn a good kernel representation, whose objective is to reveal the data similarity precisely. In this paper, we address the problem of multiple kernel learning (MKL), searching for the optimal kernel combination weights through maximizing a generalized performance measure. Most MKL methods employ the -norm simplex constraints on the kernel combination weights, which therefore involve a sparse but non-smooth solution for the kernel weights. Despite the success of their efficiency, they tend to discard informative complementary or orthogonal base kernels and yield degenerated generalization performance. Alternatively, imposing the -norm constraint on the kernel weights will keep all the information in the base kernels. This leads to non-sparse solutions and brings the risk of being sensitive to noise and incorporating redundant information. To tackle these problems, we propose a generalized MKL (GMKL) model by introducing an elastic-net-type constraint on the kernel weights. More specifically, it is an MKL model with a constraint on a linear combination of the -norm and the squared -norm on the kernel weights to seek the optimal kernel combination weights. Therefore, previous MKL problems based on the -norm or the -norm constraints can be regarded as special cases. Furthermore, our GMKL enjoys the favorable sparsity property on the solution and also facilitates the grouping effect. Moreover, the optimization of our GMKL is a convex optimization problem, where a local solution is the global optimal solution. We further derive a level method to efficiently solve the optimization problem. A series of experiments on both synthetic and real-world datasets have been conducted to show the effectiveness and efficiency of our GMKL.

Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally

In this paper, we propose a novel large margin classifier, called the maxi-min margin machine (M4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector machine (SVM), considers data only locally, while another significant model, the minimax probability machine (MPM), focuses on building the decision hyperplane exclusively based on the global information. As a major contribution, we show that SVM yields the same solution as M4 when data satisfy certain conditions, and MPM can be regarded as a relaxation model of M4. Moreover, based on our proposed local and global view of data, another popular model, the linear discriminant analysis, can easily be interpreted and extended as well. We describe the M4 model definition, provide a geometrical interpretation, present theoretical justifications, and propose a practical sequential conic programming method to solve the optimization problem. We also show how to exploit Mercer kernels to extend M4 for nonlinear classifications. Furthermore, we perform a series of evaluations on both synthetic data sets and real-world benchmark data sets. Comparison with SVM and MPM demonstrates the advantages of our new model.

Imbalanced learning with a biased minimax probability machine

Imbalanced learning is a challenged task in machine learning. In this context, the data associated with one class are far fewer than those associated with the other class. Traditional machine learning methods seeking classification accuracy over a full range of instances are not suitable to deal with this problem, since they tend to classify all the data into a majority class, usually the less important class. In this correspondence, the authors describe a new approach named the biased minimax probability machine (BMPM) to deal with the problem of imbalanced learning. This BMPM model is demonstrated to provide an elegant and systematic way for imbalanced learning. More specifically, by controlling the accuracy of the majority class under all possible choices of class-conditional densities with a given mean and covariance matrix, this model can quantitatively and systematically incorporate a bias for the minority class. By establishing an explicit connection between the classification accuracy and the bias, this approach distinguishes itself from the many current imbalanced-learning methods; these methods often impose a certain bias on the minority data by adapting intermediate factors via the trial-and-error procedure. The authors detail the theoretical foundation, prove its solvability, propose an efficient optimization algorithm, and perform a series of experiments to evaluate the novel model. The comparison with other competitive methods demonstrates the effectiveness of this new model

Efficient online learning for multitask feature selection

Learning explanatory features across multiple related tasks, or MultiTask Feature Selection (MTFS), is an important problem in the applications of data mining, machine learning, and bioinformatics. Previous MTFS methods fulfill this task by batch-mode training. This makes them inefficient when data come sequentially or when the number of training data is so large that they cannot be loaded into the memory simultaneously. In order to tackle these problems, we propose a novel online learning framework to solve the MTFS problem. A main advantage of the online algorithm is its efficiency in both time complexity and memory cost. The weights of the MTFS models at each iteration can be updated by closed-form solutions based on the average of previous subgradients. This yields the worst-case bounds of the time complexity and memory cost at each iteration, both in the order of O(d × Q), where d is the number of feature dimensions and Q is the number of tasks. Moreover, we provide theoretical analysis for the average regret of the online learning algorithms, which also guarantees the convergence rate of the algorithms. Finally, we conduct detailed experiments to show the characteristics and merits of the online learning algorithms in solving several MTFS problems.

Non-fixed and asymmetrical margin approach to stock market prediction using Support Vector Regression

Recently, support vector regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are non-stationary and noisy in nature. The volatility, usually time-varying, of the time series is therefore some valuable information about the series. Previously, we had proposed to use the volatility to adaptively change the width of the margin of SVR. We have noticed that upside margin and downside margin do not necessary be the same, and we have observed that their choice would affect the upside risk, downside risk and as well as the overall prediction result. In this paper, we introduce a novel approach to adapt the asymmetrical margins using momentum. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average.

Multi-task Learning for one-class classification

In this paper, we address the problem of one-class classification. Taking into account the fact that in some applications, the given training samples are rather limited, we attempt to utilize the advantages of Multi-task Learning (MTL), where the data of related tasks may share similar structure and helpful information. We then propose an MTL framework for one-class classification. The framework derives from the one-class v-SVM and makes use of related tasks by constraining them to have similar solutions. This formulation can be cast into a second-order cone program, which achieves a global solution and is solved efficiently. Further, the framework also maintains the favorable property of the v parameter in the v-SVM, which can control the fraction of outliers and support vectors, in one-class classification. This framework also connects with several existing models. Experimental results on both synthetic and real-world datasets demonstrate the properties and advantages of our proposed model.

Online learning for multi-task feature selection

Multi-task feature selection (MTFS) is an important tool to learn the explanatory features across multiple related tasks. Previous MTFS methods fulfill this task in batch-mode training. This makes them inefficient when data come in sequence or when the number of training data is so large that they cannot be loaded into the memory simultaneously. To tackle these problems, we propose the first online learning framework for MTFS. A main advantage of the online algorithms is the efficiency in both time complexity and memory cost due to the closed-form solutions in updating the model weights at each iteration. Experimental results on a real-world dataset attest to the merits of the proposed algorithms.

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users’ moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users’ preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users’ moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user’s check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top-k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users’ preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.