211733
Using Case-Based Reasoning in Interpreting Unsupervised Inductive Learning Results
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The interpretive process is considered as an intrinsic part in OSHAM and is carried out by a combination of case-based reasoning with matching approaches in inductive learning. An experimental comparative study of some learning methods in terms of knowledge description and prediction is given.
3251340
A Knowledge Discovery System with Support for Model Selection and Visualization
The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.
1335256
Kernel Matrix Evaluation
We study the problem of evaluating the goodness of a kernel matrix for a classification task. As kernel matrix evaluation is usually used in other expensive procedures like feature and model selections, the goodness measure must be calculated efficiently. Most previous approaches are not efficient, except for Kernel Target Alignment (KTA) that can be calculated in O(n2) time complexity. Although KTA is widely used, we show that it has some serious drawbacks. We propose an efficient surrogate measure to evaluate the goodness of a kernel matrix based on the data distributions of classes in the feature space. The measure not only overcomes the limitations of KTA, but also possesses other properties like invariance, efficiency and error bound guarantee. Comparative experiments show that the measure is a good indication of the goodness of a kernel matrix.
7929717
Chance discovery and learning minority classes
Chances are viewed in chance discovery as events/situations with significant impact on human decision making. In this research context we are particularly interested in a subset of chances that are unexpected or contradictory with human common knowledge, and the human role that we consider as an essential factor in finding such chances. We first introduce the method LUPC that can learn minority classes from large unbalanced datasets. With its visualization tools as well its exclusive and inclusive constraints, LUPC allows the user to actively participate in and to incorporate background knowledge in the chance discovery process. We then present case studies in which LUPC is used to support the user in discovering significant unexpected chances from stomach cancer and hepatitis databases.
1324170
A Random Linear Network Coding Approach to Multicast
We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks
14756669
Multilevel routing with antenna avoidance
As technology advances into the nanometer territory, the antenna problem has caused significant impact on routing tools. The antenna effect is a phenomenon of plasma-induced gate oxide degradation caused by charge accumulation on conductors. It directly influences manufacturability and yield of VLSI circuits, especially in deep-submicron technology using high density plasma. Furthermore, the continuous increase of the problem size of IC routing is also a great challenge to existing routing algorithms. In this paper, we propose a novel framework for multilevel full-chip routing with antenna avoidance using a built-in jumper insertion approach. Experimental results show that our approach reduced antenna-violated gates by about 98% and also achieved 100% routing completion for all circuits.
52850401
Optimization Based Rate Control for Multicast with Network Coding
Recent advances in network coding have shown great potential for efficient information multicasting in communication networks, in terms of both network throughput and network management. In this paper, we address the problem of rate control at end-systems for network coding based multicast flows. We develop two adaptive rate control algorithms for the networks with given coding subgraphs and without given coding subgraphs, respectively. With random network coding, both algorithms can be implemented in a distributed manner, and work at transport layer to adjust source rates and at network layer to carry out network coding. We prove that the proposed algorithms converge to the globally optimal solutions for intra-session network coding. Some related issues are discussed, and numerical examples are provided to complement our theoretical analysis.
27312959
Sorting a sequence of strong kings in a tournament
A king in a tournament is a player who beats any other player directly or indirectly. According to the existence of a king in every tournament, Wu and Sheng [Inform. Process. Lett. 79 (2001) 297-299] recently presented an algorithm for finding a sorted sequence of kings in a tournament of size n, i.e., a sequence of players u1, u2,....,un such that ui → ui+1 (ui beats ui+1) and ui is a king in the sub-tournament induced by {ui, ui+1,.....,un} for each i = 1, 2,....,n - 1. With each pair u,v of players in a tournament, let b(u,v) denote the number of third players used for u to beat v indirectly. Then, a king u is called a strong king if the following condition is fulfilled: if v → u then b(u, v) > b(v,u). In the sequel, we will show that the algorithm proposed by Wu and Sheng indeed generates a sorted sequence of strong kings, which is more restricted than the previous one.
41314743
Resilient Network Coding in the Presence of Byzantine Adversaries
Network coding substantially increases network throughput. But since it involves mixing of information inside the network, a single corrupted packet generated by a malicious node can end up contaminating all the information reaching a destination, preventing decoding. This paper introduces the first distributed polynomial-time rate-optimal network codes that work in the presence of Byzantine nodes. We present algorithms that target adversaries with different attacking capabilities. When the adversary can eavesdrop on all links and jam zO links , our first algorithm achieves a rate of C - 2zO, where C is the network capacity. In contrast, when the adversary has limited snooping capabilities, we provide algorithms that achieve the higher rate of C - zO. Our algorithms attain the optimal rate given the strength of the adversary. They are information-theoretically secure. They operate in a distributed manner, assume no knowledge of the topology, and can be designed and implemented in polynomial-time. Furthermore, only the source and destination need to be modified; non-malicious nodes inside the network are oblivious to the presence of adversaries and implement a classical distributed network code. Finally, our algorithms work over wired and wireless networks.