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Future Information Retrieval, especially in connection with the internet, will incorporate the content descriptions that are generated with social network extraction technologies and preferably incorporate the probability theory for assigning the semantic. Although there is an increasing interest about social network extraction, but a little of them has a significant impact to information retrieval...
In this paper, we propose the Interval-valued Matrix Factorization (IMF) framework. Matrix Factorization (MF) is a fundamental building block of data mining. MF techniques, such as Nonnegative Matrix Factorization (NMF) and Probabilistic Matrix Factorization (PMF), are widely used in applications of data mining. For example, NMF has shown its advantage in Face Analysis (FA) while PMF has been successfully...
Data uncertainty is common in real-world applications. Various reasons lead to data uncertainty, including imprecise measurements, network latency, outdated sources and sampling errors. These kinds of uncertainties have to be handled cautiously, or else the data mining results could be unreliable or wrong. In this demo, we will show uRule, a new rule-based classification and prediction system for...
Rough set theory is an important technique for knowledge discovery in databases. The measurement of the uncertainty of knowledge is one of the important issues in rough set theory. The definitions of entropy and the conditional entropy in the process of probability are given, and the meanings of entropy and the conditional entropy are explained in this paper. In addition, the new definition of the...
Many applications today need to manage data that is uncertain, such as information extraction (IE), data integration, sensor RFID networks, and scientific experiments. Top-k queries are often natural and useful in analyzing uncertain data in those applications. In this paper, we study the problem of answering top-k queries in a probabilistic framework from a state-of-the-art statistical IE model-semi-Conditional...
Data mining methods have been proven effective in extracting knowledge from existing data sources for the classification of soils. Previous studies have suggested that soils are spatial entities with fuzzy boundaries and prompted the development of data mining methods to extract knowledge that allows for fuzzy classifications of soils. This paper first looks at the nature of soil classification from...
Uncertainty plays an important role in clustering. For example in customer segmentation we may be faced with the situation that a certain customer not necessarily belongs to just one segment, i.e. his/her class assignment is uncertain. Several cluster algorithms have been proposed that employ uncertainty modeling in different ways. The most frequently used techniques are probability theory, fuzzy...
Business Intelligence (BI) over unstructured text is under intense scrutiny both in industry and research. Recent work in this field includes automatic integration of unstructured text into BI systems, model recognition, and probabilistic databases to handle uncertainty of Information Extraction (IE) results. Our aim is to use analytics to discover statistically relevant and unknown relationship between...
Since the highly conflicting evidence could not be combined effectively through D-S evidence theory, a novel D-S data fusion method based on evidence quality is introduced in this paper. The concrete algorithm for the reliability of observer and the measure of quality function value based on observer reliability are proposed. In the data fusion, adopting the measure of evidence quality, the collected...
Firstly, by preprocessing classification rule, we account distinct outlier attributes subspace of the rules about classification rules attributes, then it uses attribute weight vector to calculate weighted distance; secondly, it analyzes subspace outlier influence factor of weighted neighborhood area; finally, we creates frequent matching Sub-Set by comparing with subspace outlier influence factor...
Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements, etc. With uncertainty, the value of each data item is represented by a probability distribution function (pdf). In this paper, we propose a novel naive Bayes classification...
In prediction error identification, confidence regions are most commonly derived from the asymptotic statistical properties of the parameter estimator. Therefore, these confidence regions are only asymptotically valid and, for finite samples, their actual coverage rate can be smaller than the desired coverage rate. In this paper, we analyze the influence of the SNR and of the type of model structure...
We consider decision making in a Markovian setup where the reward parameters are not known in advance. Our performance criterion is the gap between the performance of the best strategy that is chosen after the true parameter realization is revealed and the performance of the strategy that is chosen before the parameter realization is revealed. We call this gap the parametric regret. We consider two...
A hierarchical Bayesian fuzzy inference nets realtime internal fault diagnostic system for induction motor is proposed. The membership functions and symptom-fault mapping relationship for motor fault diagnosis are obtained from pre-measured site experimental data as well as experts' diagnostic experience/knowledge to distinguish the effect of true fault from various external static factors. With the...
The study of multi-objective optimization has matured to a level where uncertainty is considered when comparing and evaluating solutions for any given problem. This paper reviews the current techniques that have been proposed to include uncertainty within a multi-objective framework. Probabilistic as well as fuzzy methods are reviewed. A new method to identify sample representative solutions from...
Assessing air traffic complexity on a mid term horizon can help to timely identify those safety-critical encounter situations that would require many tactical resolution maneuvers to be resolved. This is particularly useful in advanced autonomous air traffic management systems, where aircraft are responsible for self-separation maintenance. In this paper, we propose a new method to evaluate mid term...
This paper proposes a probabilistic unit commitment (UC) formulation to incorporate uncertain PV power or other intermittent energy sources using stochastic programming framework. The objective of this problem is to minimize the expected total cost during the decision time range. The proposed method is implemented to a small 9-bus test system using CPLEX and MATLAB. Several case studies are performed...
Information Fusion is a valid way which can decrease the uncertainty of making decision, and is also a hotspot. The paper makes some work on a important problem about Fuzzy Integral, that is how to get the Fuzzy Density, and compares two typical means. Based on 11 UCI data set, this paper conducts the compared experiment of several Information Fusion methods. It is compared with references 4 and 5...
In view of the uncertainties in off-line available transfer capability (ATC) evaluation, a method based on blind number was proposed. The study on ATC offers important reference information for secure operation of power system, decision-making of power market participants, system planning and so on. The representation of ATC will be more accurate with full consideration of uncertainties. Blind number...
The main focus of this paper is for decision analysis from the target-oriented point of view. Firstly, the target achievement computation method is revised, in which the resulting value function can have four shapes: concave, convex, S-shaped, inverse S-shaped. In addition, it is now more and more widely acknowledged that all facets of uncertainty cannot be captured by a single probability distribution...
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