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Learning in a non-stationary environment and in the presence of class imbalance has been receiving more recognition from the computational intelligence community, but little work has been done to create an algorithm or a framework that can handle both issues simultaneously. We have recently introduced a new member to the Learn++ family of algorithms, Learn++.NSE, which is designed to track non-stationary...
The study of host pathogen protein-protein interactions (PPIs) is essential to understand the disease-causing mechanisms of human pathogens. A large number of scientific findings about PPIs are generated in the biomedical literatures. Building a document classification system can accelerate the process of mining and curation of PPI knowledge. With more and more imbalanced dataset appearing, how to...
Since the amount of videos on the internet is huge and continuously increases, it is impossible to pre-index events in these videos. Thus, we extract the definition of each event from example videos provided as a query. But, different from positive examples, it is impractical to manually provide a variety of negative examples. Hence, we use "partially supervised learning'' where the definition...
Models for fault diagnosis can help reduce the time taken to accurately identify faults, but the complexity of modern enterprise systems means that the process of manually model-building is itself very time-consuming. We study here the relevance of bootstrapping a diagnostic model that can then be manually refined and augmented by domain experts. We present an approach to model construction, developed...
We present Bautext, a new minimally supervised approach for automatically extracting ratable aspects from customer reviews and classifying them to some previously defined categories. Bautext requires a small amount of seed words as supervised data and uses a bootstrapping mechanism o progressively collect new member for each category. Learning new category members and the category-specific terms for...
Knowledge of the statistical interactions between the attributes in a data set provides insight into the underlying structure of the data and explains the relationships (independence, synergy, redundancy) between the attributes. In a supervised learning problem, normally, a small subset of the classifying attributes are actually associated with the class label. Interaction information among the attributes...
Feature select ion is an important problem in the fields of machine learning and pat tern recognition. Data stream data classification with high dimensional and sparse, and the dimension of the need for compression, feature selection methods suitable for data stream classification study of very value of this area is currently a lack of in-depth study. This paper summarizes the current data flow classification...
In this paper a scalability test over eleven scalable benchmark functions, provided by the current workshop (Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems-A Scalability Test), are conducted for accelerated DE using generalized opposition-based learning (GODE). The average error of the best individual in the population has been reported for dimensions 50, 100,...
In this paper a new learning scheme for SAT is proposed. The originality of our approach arises from its ability to achieve clause learning even if no conflict occurs. This clearly contrasts with all the traditional learning approaches which generally refer to conflict analysis. To make such learning possible, relevant clauses, taken from the satisfied part of the formula are conjointly used with...
Object categorization has become active in the field of pattern recognition. There are two main factors which affect the performance of classification. One is the representation of images, and the other is the design of classifier. The representation of images based on bag-of-word (BOW) has become a popular method because of its simpleness and high efficiency. This paper aims to compare some state-of-the-art...
Soil erosion is one of the most typical natural disasters in China. However, due to the limitation of current technology, the investigation of soil erosion through remote sensing images is currently by human beings manually which depends on human interpretation and interactive selection. The work burden is so heavy that errors are usually inevitably unavoidable. This paper proposes the technique of...
Semi-supervised learning aims to utilize unlabeled data in the process of supervised learning. In particular, combining semi-supervised learning with dimension reduction can reduce overfitting caused by small sample size in high dimensional data. By graph representation with similarity edge weights among data samples including both labeled and unlabeled data, statistical and geometric-structures in...
Tag services have recently become one of the most popular Internet services on the World Wide Web. Due to the fact that a Web page can be associate with multiple tags, previous research on tag recommendation mainly focuses on improving its accuracy or efficiency through multi-label learning algorithms. However, as a Web page can also be split into multiple sections and be represented as a bag of instances,...
Cognitive learning factor is an important parameter in particle swarm optimization algorithm(PSO). Although many selection strategies have been proposed, there is still much work need to do. Inspired by the black stork foraging process, this paper designs a new cognitive selection strategy, in which the whole swarm is divided into adult and infant particle, and each kind particle has its special choice...
These years, P2P applications have multiplied, evolved and take a big part of Internet traffic workload. Identifying the P2P traffic and understanding their behavior is an important field. Some port, payload and transport layer feature based methods were proposed. P2P traffic identification methods by examining user payload or well-defined port numbers no longer adapt to current P2P applications....
In recent years, more and more teachers and learners have used e-learning system as the site to teach and study. Therefore, how to improve the efficiency in e-learning system has become a very important topic. This research combines Hopfield-Tank neural network and fuzzy ranking theories to analyze the learning efficiency and the useful nodes in learning path. After that, the researcher can improve...
Digital watermarking became a key technology for protecting copyrights. In this paper, we propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches,...
This paper deals with using density ensembles methods to enhance continuous estimation of distribution algorithms. In particular, two density ensembles methods are applied: one is resampling method and the other is subspaces method. In resampling continuous estimation of distribution algorithms, a population of densities is obtained by resampling operator and density estimation operator, and new candidate...
This paper introduces a method of learning kernel by fuzzy equivalence relation (FER) based on prior knowledge. Firstly, prior knowledge is represented through fuzzy membership functions and fuzzy inference rules. Consequently features of prior knowledge are obtained by proper inference methods. Secondly, the learning rules of FER-kernel are obtained in terms of FER semantic interpretation and fuzzy...
Boosting is a machine learning technique that combines several weak classifiers to improve the overall accuracy. A well known algorithm based on boosting is AdaBoost. Boosting at start (BAS) is a boosting framework that generalizes AdaBoost by allowing any initial weight distribution. BAS Committee is a scheme that uses feature clustering to determine the best weight assignments in the BAS framework...
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