A new probabilistic and entropy fusion approach for management of information sources

Fassinut-Mombot, B.; Choquel, Jean-Bernard

doi:10.1016/j.inffus.2003.06.001

Cited by 31 publications

(11 citation statements)

References 20 publications

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“…This strategy can be regarded as the simplest form of sequential search, where, at each iteration, the best sensor is incorporated into the candidate sensor set until there is no improvement in the value of the objective function. A more complex sequential search approach, called entropy adaptative aggregation algorithm, is proposed in [12]. It includes an aggregative phase to heuristically choose the initial subset and an adaptative phase to iteratively aggregate and disaggregate the current subset until it converges.…”

Section: B Optimization Methods For Sensor Selectionmentioning

confidence: 99%

Approximate Nonmyopic Sensor Selection via Submodularity and Partitioning

Liao

Wallace

2009

IEEE Trans. Syst., Man, Cybern. A

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Abstract-As sensors become more complex and prevalent, they present their own issues of cost effectiveness and timeliness. It becomes increasingly important to select sensor sets that provide the most information at the least cost and in the most timely and efficient manner. Two typical sensor selection problems appear in a wide range of applications. The first type involves selecting a sensor set that provides the maximum information gain within a budget limit. The other type involves selecting a sensor set that optimizes the tradeoff between information gain and cost. Unfortunately, both require extensive computations due to the exponential search space of sensor subsets. This paper proposes efficient sensor selection algorithms for solving both of these sensor selection problems. The relationships between the sensors and the hypotheses that the sensors aim to assess are modeled with Bayesian networks, and the information gain (benefit) of the sensors with respect to the hypotheses is evaluated by mutual information. We first prove that mutual information is a submodular function in a relaxed condition, which provides theoretical support for the proposed algorithms. For the budget-limit case, we introduce a greedy algorithm that has a constant factor of (1 − 1/e) guarantee to the optimal performance. A partitioning procedure is proposed to improve the computational efficiency of the algorithms by efficiently computing mutual information as well as reducing the search space. For the optimal-tradeoff case, a submodular-supermodular procedure is exploited in the proposed algorithm to choose the sensor set that achieves the optimal tradeoff between the benefit and cost in a polynomial-time complexity.

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Section: B Optimization Methods For Sensor Selectionmentioning

confidence: 99%

Approximate Nonmyopic Sensor Selection via Submodularity and Partitioning

Liao

Wallace

2009

IEEE Trans. Syst., Man, Cybern. A

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“…These latter strategies utilize the redundancy in information sources. Since the sum of complementarity and redundancy of a source equals a constant, it is only possible to optimize a fusion system in favor of the one or the other [3].…”

Section: General Overview On Information Fusionmentioning

confidence: 99%

“…The Washington database contains 21 image classes of locations like Australia and Italy, but also semantic concepts like football and cherry trees. Most of the images are manually annotated with a few keywords (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). Classes that with no annotation were left out in the tests, so we experimented with 16 classes, containing in all 675 images that are nearly equally distributed over the classes.…”

Section: Data Analysis Towards Effective Multimedia Information Retrimentioning

confidence: 99%

Information Fusion in Multimedia Information Retrieval

Kludas

Bruno

Marchand-Maillet

2008

Lecture Notes in Computer Science

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Abstract. In retrieval, indexing and classification of multimedia data an efficient information fusion of the different modalities is essential for the system's overall performance. Since information fusion, its influence factors and performance improvement boundaries have been lively discussed in the last years in different research communities, we will review their latest findings. They most importantly point out that exploiting the feature's and modality's dependencies will yield to maximal performance. In data analysis and fusion tests with annotated image collections this is undermined.

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“…Besides, Fassnut-Mombot and Coquel established a new probabilistic fusion methodology based on Shannon's entropy, whose goal is to reduce the combination space by explicitly representing the notions of source redundancy and source complementarity in form of entropy measures. This fusion methodology called Entropy Fusion Model [3]. Kokar et al provides an outline of a formalization of classes of information fusion systems in terms of category theory and formal languages [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Dempster-Shafer evidence theory of information fusion based on info-evolutionary value for e-business with continuous improvement

Tian

Bao

2005

IEEE International Conference on E-Business Engineering (ICEBE'05)

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Continuous improvement is the typical field of ebusiness information fusion in terms of multi-source data. This research proposes a hybrid Dampster-Shafer method with entropy and information value coevolutionary for desegregation data. The result shows, the modified theory is a multi-criteria decision tool for information fusion in decreasing the information uncertainty effectively, which can advance the ebusiness performance of continuous improvement process and quality control.

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A new probabilistic and entropy fusion approach for management of information sources

Cited by 31 publications

References 20 publications

Approximate Nonmyopic Sensor Selection via Submodularity and Partitioning

Approximate Nonmyopic Sensor Selection via Submodularity and Partitioning

Information Fusion in Multimedia Information Retrieval

Dempster-Shafer evidence theory of information fusion based on info-evolutionary value for e-business with continuous improvement

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