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Abstract: In this position paper, we are discussing achievements and open issues in the interpretability of evolving fuzzy systems (EFS). In addition to pure on-line complexity reduction approaches, which can be an important direction for increasing the transparency of the evolved fuzzy systems, we examine the state-of-the-art and provide further investigations and concepts regarding the following interpretability aspects: distinguishability, simplicity, consistency, coverage and completeness, feature importance levels, rule importance levels and interpretation of consequents. These are well-known and widely accepted criteria for the interpretability of expert-based and standard data-driven fuzzy systems in batch mode. So far, most have been investigated only rudimentarily in the context of evolving fuzzy systems, trained incrementally from data streams: EFS have focussed mainly on precise modeling, aiming for models of high predictive quality. Only in a few cases, the integration of complexity reduction steps has been handled. This paper thus seeks to close this gap by pointing out new ways of making EFS more transparent and interpretable within the scope of the criteria mentioned above. The role of knowledge expansion, a peculiar concept in EFS, will be also addressed. One key requirement in our investigations is the availability of all concepts for on-line usage, which means they should be incremental or at least allow fast processing. [Copyright &y& Elsevier]

In recent years, extensive research has been carried out by using association rules to build more accurate classifiers. The idea behind these integrated approaches is to focus on a limited subset of association rules. This paper aims to contribute to this integrated framework by adapting the Classification Based on Associations (CBA) algorithm. CBA was adapted by coupling it with another measurement of the quality of association rules: i.e. intensity of implication. The new algorithm has been implemented and empirically tested on an authentic financial dataset for purposes of bankruptcy prediction. We validated our results with an association ruleset, with C4.5, with original CBA and with CART by statistically comparing its performance via the area under the ROC-curve. The adapted CBA algorithm presented in this paper proved to generate significantly better results than the other classifiers at the 5% level of significance. [ABSTRACT FROM AUTHOR]

Abstract: Recently, a number of covering schemes proposed to exploit embedding capacity in covering images using matrix encoding. When binary secret data include a high frequency of repeated bit-blocks, a preprocessing phase is employed to compress secret data prior to the embedding process regardless of whether the compressed secret data still contain some repeated bit-blocks. This paper proposes two novel compact covering schemes, compact covering scheme (CCS) and intelligent compact covering scheme (ICCS), which directly compress and conceal the repeated bit-blocks of secret data bits into covering images within a single phase. The proposed schemes can exploit the embedding capacity and increase the embedding efficiency by reducing the required encoding operations. In addition, the proposed ICCS scheme can indicate up to which degree of repeated bit-blocks of secret data it can take advantage of in order to improve the embedding capacity. Experimental results demonstrate that the proposed ICCS method can significantly improve the embedding capacity when the ratio of the repeated bit-blocks over the total number of blocks is low. The proposed schemes are feasible for actual data-hiding applications. [Copyright &y& Elsevier]

Abstract: Web 2.0 technologies, such as forums and wikis, are enabling an explosion of global knowledge sharing through distributed large-scale conversations, but they seem to be less successful at supporting collaborative deliberation around complex and controversial questions. In order to cope with this limitation, many scholars have proposed to adopt on-line argumentation platforms to improve information visualization, organization and reuse. However, such research has mostly focused on the design of adequate argument-based knowledge formalisms. Less attention has been paid to the empirical analysis of actual interactions mediated by argumentation technology with reasonably large user communities. In this paper, we present an in-depth analysis of the data obtained in the empirical test of an argumentation platform where a 160-member community created, in 3weeks, what is to our knowledge the largest single online argument map ever built (around 5000 posts). Our results show that (i) users were able to quickly and comprehensively explore and map the debate on the selected discussion topic; (ii) substantial moderation was needed to ensure that the argument map was well-organized and users were confident with the argumentation formalism; (iii) considerable out-of-the map communication occurred, possibly as a way to allow for conversational flows inhibited by the argumentation formalism, (iv) formal rating of contributions favored exploration of the map, understanding the debate structure, and improving the quality of content. [Copyright &y& Elsevier]

Abstract: Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users. The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem. We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets. [Copyright &y& Elsevier]

Abstract: Usually the data generation rate of a data stream is unpredictable, and some data elements of the data stream cannot be processed in real time if the generation rate exceeds the capacity of a data stream processing algorithm. In order to overcome this situation gracefully, a load shedding technique is recommended. This paper proposes a frequency-based load shedding technique over a data stream of tuples. In many data stream processing applications, such as mining frequent patterns, data elements having high frequency can be considered more significant than others having low frequency. Based on this observation, in the proposed technique, only frequent elements of a data stream are processed in real time while the others are trimmed. The decision to shed a load from the data stream or not is controlled automatically by the data generation rate of a data stream. Consequently, an unnecessary load shedding operation is not allowed in the proposed technique. [Copyright &y& Elsevier]

During past years Dragomir has contributed a lot of work providing different kinds of bounds on the distance, information and divergence measures. In this paper, we have unified some of his results using the relative information of type s and relating it with the Csiszár's f-divergence. [ABSTRACT FROM AUTHOR]

In many information retrieval applications, it is necessary to be able to adopt a trie search for looking at the input character by character. As a fast and compact data structure for a trie, a double-array is presented. However, the insertion time is not faster than other dynamic retrieval methods because the double-array is a semi-static retrieval method that cannot treat high frequent updating. Further, the space efficiency of the double-array degrades with the number of deletions because it keeps empty elements produced by deletion. This paper presents a fast insertion algorithm by linking empty elements to find inserting positions quickly and a compression algorithm by reallocating empty elements for each deletion. From the simulation results for 100 thousands keys, it turned out that the insertion time and the space efficiency are achieved. [Copyright &y& Elsevier]