Attribute reduction is a critical topic in the field of rough set theory. Currently, to further enhance the stability of the derived reduct, various attribute selectors are designed based on the ...framework of ensemble selectors. Nevertheless, it must be pointed out that some limitations are concealed in these selectors: (1) rely heavily on the distribution of samples; (2) rely heavily on the optimal attribute. To generate the reduct with higher stability, a novel beam-influenced selector (BIS) is designed based on the strategies of random partition and beam. The scientific novelty of our selector can be divided into two aspects: (1) randomly partition samples without considering the distribution of samples; (2) beam-based selections of features can save the selector from the dependency of the optimal attribute. Comprehensive experiments using 16 UCI data sets show the following: (1) the stability of the derived reducts may be significantly enhanced by using our selector; (2) the reducts generated based on the proposed selector can provide competent performance in classification tasks.
Attribute reduction is commonly referred to as the key topic in researching rough set. Concerning the strategies for searching reduct, though various heuristics based forward greedy searchings have ...been developed, most of them were designed for pursuing one and only one characteristic which is closely related to the performance of reduct. Nevertheless, it is frequently expected that a justifiable searching should explicitly involves three main characteristics: (1) the process of obtaining reduct with low time consumption; (2) generate reduct with high stability; (3) acquire reduct with competent classification ability. To fill such gap, a hybrid based searching mechanism is designed, which takes the above characteristics into account. Such a mechanism not only adopts multiple fitness functions to evaluate the candidate attributes, but also queries the distance between attributes for determining whether two or more attributes can be added into the reduct simultaneously. The former may be useful in deriving reduct with higher stability and competent classification ability, and the latter may contribute to the lower time consumption of deriving reduct. By comparing with 5 state-of-the-art algorithms for searching reduct, the experimental results over 20 UCI data sets demonstrate the effectiveness of our new mechanism. This study suggests a new trend of attribute reduction for achieving a balance among various characteristics.
Copper solvent extraction entrained and dissoluted organics (SX organics) in the raffinate during SX operation can contaminated chalcopyrite ores and influence bioleaching efficiency by raffinate ...recycling. The adsorption and bioleaching of A. ferrooxidans and L. ferriphilum with contaminated ores were investigated. The results showed that, A. ferrooxidans and L. ferriphilum cells could adsorb quickly on minerals, the adsorption rate on contaminated ores were 83% and 60%, respectively, larger than on uncontaminated ores. However, in the bioleaching by the two kinds of acid bacterias, contaminated ores presented a lower bioleaching efficiency.
Learning from imbalanced data is a challenging task in the fields of machine learning and data mining. As an effective and efficient solution, cost-sensitive learning has been widely adopted to ...address class imbalance learning (CIL) problems. Weighted extreme learning machine (WELM), which is constructed based on ELM, is a significant member in the cost-sensitive-learning algorithmic family. WELM can effectively deal with CIL problems. However, it has two main drawbacks: 1) it has high time complexity on large-scale data since a large-matrix multiplication operation is required in the solution procedure and 2) it lacks flexibility since it can only tune the training error for each instance and not for each class label. In this paper, we present an alternative to WELM, which is called label-WELM (LW-ELM). Unlike WELM, LW-ELM copes with CIL problems by tuning the training error of each class label. Specifically, the expected output (or training class label) that corresponds to the minority class is augmented, thereby providing stronger tolerance to training errors of the minority-class instances. In this paper, we design two types of weight allocation strategies, both of which are based on the class-imbalance ratio (CIR). In contrast with WELM, LW-ELM is fast and flexible, where fast means that it has low-time complexity and flexible indicates that it can also be used to tackle imbalanced multi-label learning problems, while WELM cannot. The experimental results on binary-class, multiclass, and multi-label data sets with skewed class distributions show the effectiveness and superiority of the proposed LW-ELM algorithm.
A simple multigranulation rough set approach is to approximate the target through a family of binary relations. Optimistic and pessimistic multigranulation rough sets are two typical examples of such ...approach. However, these two multigranulation rough sets do not take frequencies of occurrences of containments or intersections into account. To solve such problem, by the motivation of the multiset, the model of the multiple multigranulation rough set is proposed, in which both lower and upper approximations are multisets. Such two multisets are useful when counting frequencies of occurrences such that objects belong to lower or upper approximations with a family of binary relations. Furthermore, not only the concept of approximate distribution reduct is introduced into multiple multigranulation rough set, but also a heuristic algorithm is presented for computing reduct. Finally, multiple multigranulation rough set approach is tested on eight UCI (University of California—Irvine) data sets. Experimental results show: 1. the approximate quality based on multiple multigranulation rough set is between approximate qualities based on optimistic and pessimistic multigranulation rough sets; 2. by comparing with optimistic and pessimistic multigranulation rough sets, multiple multigranulation rough set needs more attributes to form a reduct.
With the expansion of data scale and diversity, the issue of class imbalance has become increasingly salient. The current methods, including oversampling and under-sampling, exhibit limitations in ...handling complex data, leading to overfitting, loss of critical information, and insufficient interpretability. In response to these challenges, we propose a broad TSK fuzzy classifier with a simplified set of fuzzy rules (B-TSK-FC) that deals with classification tasks with class-imbalanced data. Firstly, we select and optimize fuzzy rules based on their adaptability to different complex data to simplify the fuzzy rules and therefore improve the interpretability of the TSK fuzzy sub-classifiers. Secondly, the fuzzy rules are weighted to protect the information demonstrated by minority classes, thereby improving the classification performance on class-imbalanced datasets. Finally, a novel loss function is designed to derive the weights for each TSK fuzzy sub-classifier. The experimental results on fifteen benchmark datasets demonstrate that B-TSK-FC is superior to the comparative methods from the aspects of classification performance and interpretability in the scenario of class imbalance.
Microarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes but only a few hundreds of samples or less. Such extreme asymmetry between the ...dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic. Therefore, it has been shown that selecting a small set of marker genes can lead to improved classification accuracy. In this paper, a simple modified ant colony optimization （ACO） algorithm is proposed to select tumorelated marker genes, and support vector machine （SVM） is used as classifier to evaluate the performance of the extracted gene subset. Experimental results on several benchmark tumor microarray datasets showed that the proposed approach produces better recognition with fewer marker genes than many other methods. It has been demonstrated that the modified ACO is a useful tool for selecting marker genes and mining high dimension data
Abstract
Background
To investigate the clinical value of CT-guided percutaneous fine-needle aspiration biopsy for peritoneal lesions of unknown nature.
Methods
A retrospective analysis was conducted ...of 84 patients with peritoneal thickening for unknown reasons. There were 26 males and 58 females who underwent CT-guided percutaneous fine-needle aspiration biopsy for peritoneal lesions.
Result
Among these 84 patients, no definite pathologic diagnosis was made in 3 patients, who were lost to the follow-up. The accuracy rate of CT-guided percutaneous fine-needle aspiration biopsy was 95.1% (77/81). Sixty lesions were pathologically-diagnosed with malignancies (74.1%), including 55 with peritoneal metastases, 4 with malignant mesotheliomas, and 1 with a lymphoma. Twenty-four patients (33.8%) were diagnosed as benign lesions, including 11 with tuberculosis and 13 with inflammatory lesions. The complications of CT-guided percutaneous fine-needle aspiration biopsy included bleeding in 1 patient and ascites leakage in 2 patients.
Conclusion
CT-guided percutaneous fine-needle aspiration biopsy is a safe and effective method for diagnosing peritoneal lesions.
In online-learning, the data is incrementally received and the distributions from which it is drawn may keep changing over time. This phenomenon is widely known as concept drift. Such changes may ...affect the generalization of a learned model to future data. This problem may be exacerbated by the form of the drift itself changing over time. Quantitative measures to describe and analyze the concept drift have been proposed in previous work. A description composed from these measures is called a concept drift map. We believe that these maps could be useful for guiding how much knowledge in the old model should be forgotten. Therefore, this paper presents an adaptive online learning model that uses a concept drift map to regulate the forgetting factor of an extreme learning machine. Specifically, when a batch of new instances are labeled, the distribution of each class on each attribute is firstly estimated, and then it is compared with the distribution estimated in the previous batch to calculate the magnitude of concept drift, which is further used to regulate the forgetting factor and to update the learning model. Therefore, the novelty of this paper lies in that a quantitative distance metric between two distributions constructed on continuous attribute space is presented to construct concept drift map which can be further associated with the forgetting factor to make the learning model adapt the concept drift. Experimental results on several benchmark stream data sets show the proposed model is generally superior to several previous algorithms when classifying a variety of data streams subject to drift, indicating its effectiveness and feasibility.
Fuzzy support vector machine (FSVM) has been combined with class imbalance learning (CIL) strategies to address the problem of classifying skewed data. However, the existing approaches hold several ...inherent drawbacks, causing the inaccurate prior data distribution estimation, further decreasing the quality of the classification model. To solve this problem, we present a more robust prior data distribution information extraction method named relative density, and two novel FSVM-CIL algorithms based on the relative density information in this paper. In our proposed algorithms, a K-nearest neighbors-based probability density estimation (KNN-PDE) alike strategy is utilized to calculate the relative density of each training instance. In particular, the relative density is irrelevant with the dimensionality of data distribution in feature space, but only reflects the significance of each instance within its class; hence, it is more robust than the absolute distance information. In addition, the relative density can better seize the prior data distribution information, no matter the data distribution is easy or complex. Even for the data with small injunctions or a large class overlap, the relative density information can reflect its details well. We evaluated the proposed algorithms on an amount of synthetic and real-world imbalanced datasets. The results show that our proposed algorithms obviously outperform to some previous work, especially on those datasets with sophisticated distributions.
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