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Most classification approaches aim at achieving high prediction accuracy on a given dataset. However, in most practical cases, some action, such as mailing an offer or treating a patient, is to be taken on the classified objects and we should model not the class probabilities themselves, but instead, the change in class probabilities caused by the action. The action should then be performed on those...
Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that...
Time series prediction algorithms are widely used for applications such as demand forecasting, weather forecasting and many others to make well informed decisions. In this paper, we compare the most prevalent of these methods as well as suggest our own, where the time series are generated from highly complex industrial processes. These time series are non-stationary and the relationships between the...
We propose a framework for learning generalized additive models at very little additional cost (a small constant) compared to some of the most efficient schemes for learning linear classifiers such as linear SVMs and regularized logistic regression. We achieve this through a simple feature encoding scheme followed by a novel approach to regularization which we term ``generalized lasso''. Addtive models...
Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global...
Warehouse data center is very large scale and complex, which constains tens of thousands servers and accomodates various applications. What's more important, energy consumption has risen to a critical point. Scheduling needs to maintain performance and reduce energy consumption as much as possible. Previous researches have proposed RL (reinforcement learning) as a solution. These approaches have reduced...
The paper studies the application of principal component analysis and ANN (Artificial Neural Networks) for pre-warning of enterprise financial crisis, analyzes the factors of financial crisis, and constructs the model of the enterprise financial crisis with principal component analysis and ANN. It integrates simplifying of enterprise financial crisis index, dynamic learning of financial crisis knowledge...
The autonomic management of large-scale distributed systems now allows performance improvement, availability, and security, while simultaneously reducing the effort and skills required of system administrators. One way that systems can support these abilities is by relying on a continuous monitoring service to keep track of the states of the targeted systems. However, it is challenging to achieve...
A new time series prediction architecture is introduced using a fuzzy inference system (FIS) and a new framework for fuzzy relational clustering of time series. The FIS is used to predict future samples in a time series where recurrent neural networks comprise the consequents of the rules. The antecedents come in the form of fuzzy relations; however, previous approaches such as FCM build these antecedents...
Expanding mathematical models and forecasting the traffic flow is a crucial case in studying the dynamic behaviors of the traffic systems these days. Artificial Neural Networks (ANNs) are of the technologies presented recently that can be used in the intelligent transportation system field. In this paper, two different algorithms, the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF)...
Locally weighted learning (LWL), which is an effectual and flexible method for prediction problems, is widely used in many regression scenarios. The training data samples, referring to the history experience knowledge base, are required to help do regression by new queries. However, sometimes, the knowledge base tends to be helpless due to the lake of information, such as inadequate training data...
Effective software architecture evaluation methods are essential in today's system development for mission critical systems. We have previously developed MEMS and a set of test statistics for evaluating middleware architectures, which proven an effective assessment of important quality attributes and their characterizations. We have observed it is common that many system performance response data...
Efficient management of multimedia services necessitates the understanding of how the quality of these services is perceived by the users. Estimation of the perceived quality or Quality of Experience (QoE) of the service is a challenging process due to the subjective nature of QoE. This process usually incorporates complex subjective studies that need to recreate the viewing conditions of the service...
Multi-label classification is a popular learning task. However, some of the algorithms that learn from multi-label data, can only output a score for each label, so they cannot be readily used in applications that require bipartitions. In addition, several of the recent state-of-the-art multi-label classification algorithms, actually output a score vector primarily and employ one (sometimes simple)...
Ensemble pruning is concerned with the reduction of the size of an ensemble prior to its combination. Its purpose is to reduce the space and time complexity of the ensemble and/or to increase the ensemble's accuracy. This paper focuses on instance-based approaches to ensemble pruning, where a different subset of the ensemble may be used for each different unclassified instance. We propose modeling...
The data mining and machine learning community is often faced with two key problems: working with imbalanced data and selecting the best features for machine learning. This paper presents a process involving a feature selection technique for selecting the important attributes and a data sampling technique for addressing class imbalance. The application domain of this study is software engineering,...
As an effective method of machine learning, Support vector machine has been widely used in prediction. Proposition the supply of risk control and prevention, based on establishment evaluation index system and questionnaire to enterprise, this paper construct the supply risk prediction model and then discuss the fitting degree of model, expect to provide the basis for supply risk management.
In wastewater treatment plants, It's difficult to acquire online data of BOD5 (Biochemical Oxygen Demand for 5 days) due to its characteristic and unreliability of on-line sensors. Furthermore, although soft sensors models are widely used in wastewater treatment, only a few approaches for soft sensors models are designed to address the problems currently existing in the wastewater treatment. In such...
Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic patterns in application resource demands and adjust their resource allocations automatically. Our approach leverages...
The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured...
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