Yong Qi

Updating multigranulation rough approximations with increasing of granular structures

Dynamic updating of the rough approximations is a critical factor for the success of the rough set theory since data is growing at an unprecedented rate in the information-explosion era. Though many updating schemes have been proposed to study such problem, few of them were carried out in a multigranulation environment. To fill such gap, the updating of the multigranulation rough approximations is firstly explored in this paper. Both naive and fast algorithms are presented for updating the multigranulation rough approximations with the increasing of the granular structures. Different from the naive algorithm, the fast algorithm is designed based on the monotonic property of the multigranulation rough approximations. Experiments on six microarray data sets show us that the fast algorithm can effectively reduce the computational time in comparison with the naive algorithm when facing high dimensional data sets. Moreover, it is also shown that fast algorithm is useful in decreasing the computational time of finding both traditional reduct and attribute clustering based reduct.

TargetATPsite: A template‐free method for ATP‐binding sites prediction with residue evolution image sparse representation and classifier ensemble

Understanding the interactions between proteins and ligands is critical for protein function annotations and drug discovery. We report a new sequence‐based template‐free predictor (TargetATPsite) to identify the Adenosine‐5′‐triphosphate (ATP) binding sites with machine‐learning approaches. Two steps are implemented in TargetATPsite: binding residues and pockets predictions, respectively. To predict the binding residues, a novel image sparse representation technique is proposed to encode residue evolution information treated as the input features. An ensemble classifier constructed based on support vector machines (SVM) from multiple random under‐samplings is used as the prediction model, which is effective for dealing with imbalance phenomenon between the positive and negative training samples. Compared with the existing ATP‐specific sequence‐based predictors, TargetATPsite is featured by the second step of possessing the capability of further identifying the binding pockets from the predicted binding residues through a spatial clustering algorithm. Experimental results on three benchmark datasets demonstrate the efficacy of TargetATPsite.

α-Dominance relation and rough sets in interval-valued information systems

Though rough set has been widely used to study systems characterized by insufficient and incomplete information, its performance in dealing with initial interval-valued data needs to be seriously considered for improving the suitability and scalability. The aim of this paper is to present a parameterized dominance-based rough set approach to interval-valued information systems. First, by considering the degree that an interval-valued data is dominating another one, we propose the concept of α-dominance relation. Second, we present the α-dominance based rough set model in interval-valued decision systems. Finally, we introduce lower and upper approximate reducts into α-dominance based rough set for simplifying decision rules, we also present the judgement theorems and discernibility functions, which describe how lower and upper approximate reducts can be calculated. This study suggests potential application areas and new research trends concerning rough set approach to interval-valued information systems.

Kernel-SOM Based Visualization of Financial Time Series Forecasting

Visualizing the forecasting results of financial time series provides significant convenience to the user. In this paper, the disadvantages of the traditional SOM for the visualization of financial time series forecasting are discussed first and then the Kernel-SOM is proposed for better visualization performance. Experimental results demonstrated that compared with the traditional SOM, Kernel-SOM is more suitable for the visualization of financial time series forecasting

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Decision-theoretic rough set is a special rough set approach, which includes both misclassification and delayed decision costs. Though the property of monotonicity does not always hold in decision-theoretic rough set, the decision-monotonicity reduct may help us to increase both lower approximation and upper approximation. The experimental results in this paper tell us that by comparing with the original decision system, more cost is required with decision-monotonicity reduct. The implied philosophy is: if you want to get more, you should pay more!

δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions

Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yaos decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve this problem, a δ-cut decision-theoretic rough set is proposed, which is based on the δ-cut quantitative indiscernibility relation. Furthermore, with respect to criterions of decision-monotonicity and cost decreasing, two different algorithms are designed to compute reducts, respectively. The comparisons between these two algorithms show us the following: (1) with respect to the original data set, the reducts based on decision-monotonicity criterion can generate more rules supported by the lower approximation region and less rules supported by the boundary region, and it follows that the uncertainty which comes from boundary region can be decreased; (2) with respect to the reducts based on decision-monotonicity criterion, the reducts based on cost minimum criterion can obtain the lowest decision costs and the largest approximation qualities. This study suggests potential application areas and new research trends concerning rough set theory.

A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data

Accurate identification of the cancer types is essential to cancer diagnoses and treatments. Since cancer tissue and normal tissue have different gene expression, gene expression data can be used as an efficient feature source for cancer classification. However, accurate cancer classification directly using original gene expression profiles remains challenging due to the intrinsic high-dimension feature and the small size of the data samples. We proposed a new self-training subspace clustering algorithm under low-rank representation, called SSC-LRR, for cancer classification on gene expression data. Low-rank representation (LRR) is first applied to extract discriminative features from the high-dimensional gene expression data; the self-training subspace clustering (SSC) method is then used to generate the cancer classification predictions. The SSC-LRR was tested on two separate benchmark datasets in control with four state-of-the-art classification methods. It generated cancer classification predictions with an overall accuracy 89.7 percent and a general correlation 0.920, which are 18.9 and 24.4 percent higher than that of the best control method respectively. In addition, several genes (RNF114, HLA-DRB5, USP9Y, and PTPN20) were identified by SSC-LRR as new cancer identifiers that deserve further clinical investigation. Overall, the study demonstrated a new sensitive avenue to recognize cancer classifications from large-scale gene expression data.

Characterizing Hierarchies on Covering-Based Multigranulation Spaces

Hierarchy plays a fundamental role in the development of the Granular Computing(GrC). In many practical applications, the granules are formed in a family of the coverings, which can construct a Covering-based Multigranulation Space(CBMS). It should be noticed that the hierarchies on Covering-based Multigranulation Spaces has become a necessity. To solve such problem, the concepts of the union knowledge distance and the intersection knowledge distance are introduced into the CBMS, which can be used to construct the knowledge distance lattices. According to the union knowledge distance and the intersection knowledge distance, two partial orderings can be derived, respectively. The example shows that the derived partial orderings can compare the finer or coarser relationships between two different Covering-based Multigranulation Spaces effectively. The theoretical results provide us a new way to the covering based granular computing.

Multicost Decision-Theoretic Rough Sets Based on Maximal Consistent Blocks

Decision-theoretic rough set comes from Bayesian decision procedure, in which a pair of the thresholds is derived by the cost matrix for the construction of probabilistic rough set. However, classical decision-theoretic rough set can only be used to deal with complete information systems. Moreover, it does not take the property of variation of cost into consideration. To solve above two problems, the maximal consistent block is introduced into the construction of decision-theoretic rough set by using multiple cost matrixes. Our approach includes optimistic and pessimistic multicost decision-theoretic rough set models. Furthermore, the whole decision costs of optimistic and pessimistic multicost decision-theoretic rough sets are calculated in decision systems. This study suggests potential application areas and new research trends concerning decision-theoretic rough set.

Rough Set Approach to Incomplete Multiscale Information System

Multiscale information system is a new knowledge representation system for expressing the knowledge with different levels of granulations. In this paper, by considering the unknown values, which can be seen everywhere in real world applications, the incomplete multiscale information system is firstly investigated. The descriptor technique is employed to construct rough sets at different scales for analyzing the hierarchically structured data. The problem of unravelling decision rules at different scales is also addressed. Finally, the reduct descriptors are formulated to simplify decision rules, which can be derived from different scales. Some numerical examples are employed to substantiate the conceptual arguments.