Mieczyslaw A. Klopotek

Intelligent Information Processing and Web Mining

Derivation of new features of observed variables has two important goals: reduction of dimensionality and de-noising. A desired property of the derived new features is their meaningful interpretation. The SCoTLASS method (Jolliffe, Trendafilov and Uddin, 2003) offers such possibility. We explore the properties of the SCoTLASS method applied to the yeast genes data investigated in (Bartkowiak et al., 2003, 2004). All the derived features have really a simple meaningful structure: each new feature is spanned by two original variables belonging to the same block.

Modelling the Evolution of Cooperative Behavior in Ad Hoc Networks using a Game Based Model

In this paper we address the problem of cooperation and selfish behavior in ad hoc networks. We present a new game theory based model to study cooperation between nodes. This model has some similarities with the iterated prisoners dilemma under the random pairing game. In such game randomly chosen players receive payoffs that depend on the way they behave. The network gaming model includes a simple reputation collection and trust evaluation mechanisms. In our proposition a decision whether to forward or discard a packet is determined by a strategy based on the trust level in the source node of the packet and some general information about behavior of the network. A genetic algorithm (GA) is applied to evolve strategies for the participating nodes. These strategies are targeted to maximize the throughput of the network by enforcing cooperation. Experimental results show that proposed strategy based approach successfully enforces cooperation maximizing the network throughput

An immune network for contextual text data clustering

We present a novel approach to incremental document maps creation, which relies upon partition of a given collection of documents into a hierarchy of homogeneous groups of documents represented by different sets of terms. Further each group (defining in fact separate context) is explored by a modified version of the aiNet immune algorithm to extract its inner structure. The immune cells produced by the algorithm become reference vectors used in preparation of the final document map. Such an approach proves to be robust in terms of time and space requirements as well as the quality of the resulting clustering model.

Very large Bayesian multinets for text classification

This paper presents a newly developed algorithm learning very large tree-like Bayesian networks from data and exploits it to create a Bayesian multinet (BMN) classifier for natural language text documents. Results of empirical evaluation of this BMN classifier are presented. The study suggests that tree-like Bayesian networks are able to handle a classification task in 100000 variables with sufficient speed and accuracy.

Empirical Models for the Dempster-Shafer-Theory

In spite of many useful properties, the Dempster-Shafer Theory of evidence (DST) experienced sharp criticism from many sides. The basic line of criticism is connected with the relationship between the belief function (the basic concept of DST) and frequencies [65,18]. A number of attempts to interpret belief functions in terms of probabilities have failed so far to produce a fully compatible interpretation with DST — see e.g. [34,18,14] etc. As a way out of those difficulties, in the paper we will explain our three model proposals: (1) “the marginally correct approximation”, (2) “the qualitative model”, (3) “the quantitative model”. All of them fit the framework of DST, especially the Dempster rule of combination of evidence that was the hardest point and the point of failure of previously known attempts.

Preventing selfish behavior in Ad Hoc networks

Cooperation enforcement is one of the key issues in ad hoc networks. In this paper we proposes a new strategy driven approach that aims at discouraging selfish behavior among network participants. Each node is using a strategy that defines conditions under which packets are being forwarded. Such strategy is based on the notion of trust and activity of the source node of the packet. This way network participants are forced to forward packets and to reduce the amount of time spent in a sleep mode. To evaluate strategies we use a new game theory based model of an ad hoc network. A genetic algorithm (GA) is applied to find good strategies. Experimental results show that our approach makes selfish behavior unattractive.

Text data clustering by contextual graphs

In this paper, we focus on the class of graph-based clustering models, such as growing neural gas or idiotypic nets for the purpose of high-dimensional text data clustering. We present a novel approach, which does not require operation on the complex overall graph of clusters, but rather allows to shift majority of effort to context-sensitive, local subgraph and local sub-space processing. Savings of orders of magnitude in processing time and memory can be achieved, while the quality of clusters is improved, as presented experiments demonstrate.

On Some Clustering Algorithms for Document Maps Creation

In this research paper we pinpoint at the need of redesigning of the WebSOM document map creation algorithm. We insist that the SOM clustering should be preceded by identifying major topics of the document collection. Furthermore, the SOM clustering should be preceded by a pre-clustering process resulting in creation of groups of documents with stronger relationships; the groups, not the documents, should be subject of SOM clustering. We propose appropriate algorithms and report on achieved improvements.

Towards adaptive web mining: histograms and contexts in text data clustering

We present a novel approach to the growing neural gas (GNG). based clustering of the high-dimensional text data. We enhance our Contextual GNG models (proposed previously to shift the majority of calculations to context-sensitive, local sub-graphs and local sub-spaces and so to reduce computational complexity) by developing a new, histogram-based method for incremental model adaptation and evaluation of its stability.

Evolution of cooperation in ad hoc networks under game theoretic model

In this paper, we present a new approach to evolve a cooperative behavior in ad hoc networks. We propose an environment composed of four elements: a game theory based model of the network, trust evaluation mechanism, strategy that defines the behavior of each node and a genetic algorithm. Interaction between nodes is represented as in the Iterated Prisoners Dilemma under the Random Pairing game. In such a game randomly chosen players receive payoffs that depend on the way they behave. Each node is using a strategy that defines when to drop or to forward packets coming from other nodes. Such strategy is based on the past behavior of the network and on the trust level in the source node of the packet. Using the genetic algorithm we show how such strategies evolve ensuring high level of cooperation in the network.