pinecone/core-2020-05-10-deduplication huggingface dataset:
Inflating examples to obtain rules

A new machine learning system is presented in this paper. It is called Inner and induces classification rules from a set of training examples. The process followed by this system starts with the random selection of a subset of examples that are iteratively inflated in order to cover the surroundings provided that they are inhabited by examples of the same class, thus becoming rules that will be applied by means of a partial matching mechanism. The rules so obtained can be seen as clusters of examples and represent clear evidence to support explanations about their future classifications, and may be used to build intelligent advisors. The whole algorithm can be seen as a set of elastic transformations of examples and rules, and produces concise, accurate rule sets, as is experimentally demonstrated in the final section of the pape
An evolutionary learning approach for adaptive negotiation agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This\ud
paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications
Generalizing the conjunction rule for aggregating conflicting expert opinions.

In multiagent expert systems, the conjunction rule is commonly used to combine expert information represented by imprecise probabilities. However, it is well known that this rule cannot be applied in the case of expert conflict. In this article, we propose to resolve expert conflict by means of a second-order imprecise probability model. The essential idea underlying the model is a notion of behavioral trust. We construct a simple linear programming algorithm for calculating the aggregate. This algorithm explains the proposed aggregation method as a generalized conjunction rule. It also provides an elegant operational interpretation of the imprecise second-order assessments, and thus overcomes the problems of interpretation that are so common in hierarchical uncertainty models
Experiences in evaluating multilingual and text-image information retrieval

23 pages, 8 figures.One important step during the development of information retrieval (IR) processes is the evaluation of the output regarding the information needs of the user. The "high quality" of the output is related to the integration of different methods to be applied in the IR process and the information included in the retrieved documents, but how can "quality" be measured? Although some of these methods can be tested in a stand-alone way, it is not always clear what will happen when several methods are integrated. For this reason, much effort has been put into establishing a good combination of several methods or to correctly tuning some of the algorithms involved. The current approach is to measure the precision and recall figures yielded when different combinations of methods are included in an IR process. In this article, a short description of the current techniques and methods included in an IR system is given, paying special attention to the multilingual aspect of the problem. Also a discussion of their influence on the final performance of the IR process is presented by explaining previous experiences in the evaluation process followed in two projects (MIRACLE and OmniPaper) related to multilingual information retrieval.This work has been partially supported by the projects OmniPaper (European Union, 5th Framework Programme for Research and Technological Development, IST-2001-32174), NEDINE (E-Content project Ref.: 22225), and GPS Project—Software Process Management Platform: modeling, reuse, and measurement (National Research Plan, TIN2004-07083).Publicad
Two steps reinforcement learning

When applying reinforcement learning in domains with very large or continuous state spaces, the experience obtained by the learning agent in the interaction with the environment must be generalized. The generalization methods are usually based on the approximation of the value functions used to compute the action policy and tackled in two different ways. On the one hand by using an approximation of the value functions based on a supervized learning method. On the other hand, by discretizing the environment to use a tabular representation of the value functions. In this work, we propose an algorithm that uses both approaches to use the benefits of both mechanisms, allowing a higher performance. The approach is based on two learning phases. In the first one, a learner is used as a supervized function approximator, but using a machine learning technique which also outputs a state space discretization of the environment, such as nearest prototype classifiers or decision trees do. In the second learning phase, the space discretization computed in the first phase is used to obtain a tabular representation of the value function computed in the previous phase, allowing a tuning of such value function approximation. Experiments in different domains show that executing both learning phases improves the results obtained executing only the first one. The results take into account the resources used and the performance of the learned behavior.This research was partially conducted while the firs author was visiting Carnegie Mellon University from the Universidad Carlos III de Madrid, supported by a generous grant from the Spanish Ministry of Education and Fulbright. Both authors were partially sponsored by the Spanish MEC project TIN2005-08945-C06-05 and regional CAM-UC3M project number CCG06-UC3M/TIC-0831.Publicad
Algorithms of nonlinear document clustering based on fuzzy multiset model

Fuzzy multiset is applicable as a model of information retrieval because it has the mathematicalstructure that expresses the number and the degree of attribution of an element simultaneously.Therefore, fuzzy multisets can be used also as a suitable model for document clustering. Thispaper aims at developing clustering algorithms based on a fuzzy multiset model for documentclustering. The standard proximity measure of the cosine correlation is generalized in the multisetmodel, and two nonlinear clustering techniques are applied to the existing clustering methods.One introduces a variable for controlling cluster volume sizes; the other one is a kernel trick usedin support vector machines. Moreover, clustering by competitive learning is also studied. Whenthe kernel trick has been used the classification configuration of data in a high-dimensional featurespace is visualized by self-organizing maps. Two numerical examples, which use an artificial dataand real document data, are shown and effects of the proposed methods are discussed
Belief functions on lattices

International audienceWe extend the notion of belief function to the case where the underlying structure is no more the Boolean lattice of subsets of some universal set, but any lattice, which we will endow with a minimal set of properties according to our needs. We show that all classical constructions and definitions (e.g., mass allocation, commonality function, plausibility functions, necessity measures with nested focal elements, possibility distributions, Dempster rule of combination, decomposition w.r.t. simple support functions, etc.) remain valid in this general setting. Moreover, our proof of decomposition of belief functions into simple support functions is much simpler and general than the original one by Shafer
A general unified framework for pairwise comparison matrices in multicriterial methods

In a  Multicriteria Decision Making context, a pairwise comparison matrix $A=(a_{ij})$ is a helpful tool  to determine  the weighted
ranking on a set $X$ of alternatives or criteria. The entry $a_{ij}$ of the matrix can assume different meanings: $a_{ij}$ can be a preference ratio (multiplicative case) or a preference difference (additive case) or  $a_{ij}$ belongs to $[0,1]$ and measures  the
distance from the indifference that is expressed by 0.5 (fuzzy
case).  For the multiplicative case, a consistency index for the
matrix $A$ has been provided by T.L. Saaty in terms of maximum eigenvalue.

We consider pairwise comparison matrices over an abelian linearly
ordered group and, in this way, we provide a general framework
including the mentioned cases. By introducing a more general notion
of metric, we provide a consistency index  that has a natural
meaning and it is easy to compute in the additive and multiplicative cases; in the other cases, it can be computed easily starting from a suitable additive or multiplicative matrix
Human - guided fuzzy decision for image similarity analysis and classification based on information compression

This paper introduces an original unsupervised learning algorithm for information compression that is further used in the proposed fuzzy inference procedure for discovering similarities between different images for the purpose of their classification. Two features extracted from each compressed information model are used in the paper to represent the location of the compressed model in the three-dimensional red-green-blue (RGB) space and its size (volume). A method for tuning the fuzzy inference procedure is proposed in the paper that uses a predefined human preference in the form of a given list of similar images with their approximate similarity levels. Thus the whole\ud
computation scheme is a kind of human-guided similarity analysis. The choice of the optimization algorithm and the selection of the optimization criterion are among the important problems, discussed in the paper. The final goal is to achieve a plausible “human-like” decision for similarity, when processing large number of images and other pictorial information. The whole proposed computation scheme for similarity analysis and classification is illustrated on a test example of flower images followed by detailed discussions.  C � 2010 Wiley Periodicals, Inc.\u
A lazy learning approach for building classification models

In this paper, we propose a lazy learning strategy for building classification learning models. Instead of learning the models with the whole training data set before observing the new instance, a selection of patterns is made depending on the new query received and a classification model is learnt with those selected patterns. The selection of patterns is not homogeneous, in the sense that the number of selected patterns depends on the position of the query instance in the input space. That selection is made using a weighting function to give more importance to the training patterns that are more similar to the query instance. Our intention is to provide a lazy learning mechanism suited to any machine learning classification algorithm. For this reason, we study two different methods to avoid fixing any parameter. Experimental results show that classification rates of traditional machine learning algorithms based on trees, rules, or functions can be improved when they are learnt with the lazy learning approach proposed.This work has been funded by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR project).Publicad
Division Charts as Granules and Their Merging Algorithm for Rule Generation in Nondeterministic Data

We have been proposing a framework rough Nondeterministic information analysis, which considers granular computing concepts in tables with incomplete and nondeterministic information, as well as rule generation. We have recently defined an expression named division chart with respect to an implication and a subset of objects. Each division chart takes the role of the minimum granule for rule generation, and it takes the role of contingency table in statistics. In this paper, we at first define a division chart in deterministic information systems (DISs) and clarify the relation between a division chart and a corresponding implication. We also consider a merging algorithm for two division charts and extend the relation in DISs to nondeterministic information systems. The relation gives us the foundations of rule generation in tables with nondeterministic information
Construction of Intelligent Virtual Worlds Using a Grammatical Framework

The potential of integrating multiagent systems and virtual environments has not been exploited to its whole extent. This paper proposes a model based on grammars, called Minerva, to construct complex virtual environments that integrate the features of agents. A virtual world is described as a set of dynamic and static elements. The static part is represented by a sequence of primitives and transformations and the dynamic elements by a series of agents. Agent activation and communication is achieved using events, created by the so-called event generators. The grammar defines a descriptive language with a simple syntax and a semantics, defined by functions. The semantics functions allow the scene to be displayed in a graphics device, and the description of the activities of the agents, including artificial intelligence algorithms and reactions to physical phenomena. To illustrate the use of Minerva, a practical example is presented: a simple robot simulator that considers the basic features of a typical robot. The result is a functional simple simulator. Minerva is a reusable, integral, and generic system, which can be easily scaled, adapted, and improved. The description of the virtual scene is independent from its representation and the elements that it interacts with
Social network decision making with linguistic trustworthiness based induced OWA operators

The file attached to this record is the authors final peer reviewed version. The publisher's version of record can be found by following the DOI link.Classic aggregation operators in group decision making such as the OWA, IOWA, C-IOWA, P-IOWA and I-IOWA have shown to be successful tools in order to provide flexibility in the aggregation of preferences. However, these operators do not take advantage of information related to the interaction between experts. Experts involved in a group decision making problem may have developed opinions about the reliability of other experts' judgements, either because they have previous history of interaction with each other  or because they have knowledge that informs them on the reliability of other colleagues in the group in solving decision making problems in the past. In this paper, and within the framework of social network decision making, we present three new social network analysis based IOWA operators that take advantage of the linguistic trustworthiness information gathered from the experts' social network to aggregate the social group preferences. Their use is analysed with simple but illustrative examples
An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management

In this work we are proposing a trading system where fuzzy logic is applied not only for defining the trading rules, but also for managing the capital to invest. In fact, two fuzzy decision support systems are developed. The first one uses fuzzy logic to design the trading rules and to apply the stock market technical indicators. The second one enhances this fuzzy trading system adding a fuzzy strategy to manage the capital to trade. Additionally, a new technical market indicator that produces short and long entry signals is introduced. It is based on the MACD (Moving Average Convergence Divergence) indicator. Its parameters have been optimized by genetic algorithms. The proposals are compared to a classical non-fuzzy version of the pro-posed trading systems and to the Buy-and-Hold strategy. Results favor our fuzzy trading system in the two markets considered, NASDAQ100 and EUROSTOXX. Conclusions suggest that the use of fuzzy logic for capital management is promising and deserves further exploratio
Practical Methods for Constructing Possibility Distributions

This survey paper provides an overview of existing methods for building possibility distributions. We both consider the case of qualitative possibility theory, where the scale remains ordinal, and the case of quantitative possibility theory, where the scale is the real interval [0, 1]. Methods may be order-based or similarity-based for qualitative possibility distributions, whereas statistical methods apply in the quantitative case and then possibilities encode nested random epistemic sets or upper bounds of probabilities. But distance-based approaches, or expert estimates, may be also exploited in the quantitative case
Thirty years of the international journal of intelligent systems: a bibliometric review

The International Journal of Intelligent Systems was created in 1986. Today, the journal is 30 years old. To celebrate this anniversary, this study develops a bibliometric review of all of the papers published in the journal between 1986 and 2015. The results are largely based on the Web of Science Core Collection, which classifies leading bibliographic material by using several indicators including total number of publications and citations, the h-index, cites per paper, and citing articles. Thework also uses theVOS viewer software for visualizing the main results through bibliographic coupling and co-citation. The results show a general overview of leading trends that have influenced the journal in terms of highly cited papers, authors, journals, universities and countries. C  2016 Wiley Periodicals, Inc
A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority

In group decision making, there are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnose of an illness or parliament members looking for consensus on an specific law being passed. In this article, we present a method that utilizes induced ordered weighted averaging (IOWA) operators to aggregate a majority opinion from a number of sentiment analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers as typically seen in group decision situations. In this case, the numerical outputs of different SA classification methods are used as input to a specific IOWA operator that is semantically close to the fuzzy linguistic quantifier ‘most of’. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represent what the majority think. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier ‘most’ (math formula) proved to yield superior results compared to those achieved when utilizing other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques
Generating OWA weights using truncated distributions

Ordered weighted averaging (OWA) operators have been widely used in decision
making these past few years. An important issue facing the OWA operators' users
is the determination of the OWA weights. This paper introduces an OWA
determination method based on truncated distributions that enables intuitive
generation of OWA weights according to a certain level of risk and trade-off.
These two dimensions are represented by the two first moments of the truncated
distribution. We illustrate our approach with the well-know normal distribution
and the definition of a continuous parabolic decision-strategy space. We
finally study the impact of the number of criteria on the results.Comment: 7 pages, 7 figure
The acclamation consensus state and an associated ranking rule

The study of conditions, under which the existence of an “absolute” best winner can be assured, is a hot topic in the field of social choice. Unanimity is an evident example of a condition under which the winner is obvious. However, many more properties weaker than unanimity have been analysed in literature: the presence of a Condorcet winner, strong stochastic transitivity, the presence of a candidate that Borda dominates all other candidates, etc. Unfortunately, one could easily find a prominent ranking rule, for which the outcome does not agree with these relaxed conditions. In this study, we aim to identify a condition weaker than unanimity, but under which the social outcome is still obvious. This condition, defined as the conjunction of three properties already studied by the present authors and hereinafter referred to as acclamation, will be proven to be a meeting point for the most prominent ranking rules in social choice theory, and will be used for introducing an intuitively appealing ranking rule
Envisioning innovative groundwater regulation policies through scenario workshops in France and Portugal

International audienceGroundwater management policies will need to be revised in many Mediterranean countries, in light of the impact of climate change and the increasing demand for water. In this paper, we analyze stakeholder perceptions of three groundwater policy scenarios which respectively assume a strengthening of state intervention, the introduction of market regulation mechanisms, and the transfer of regulation responsibility to farmers. The method consists of organizing scenario workshops with experts, institutional representatives, and farmers. It is applied in two case studies in France and Portugal. From a methodological viewpoint, the research demonstrates the farmers' ability to contribute to an exploratory assessment of possible future water management scenarios. From a policy viewpoint it clarifies expectations concerning State intervention and self-regulation by farmers. It also provides some insights regarding the acceptability of tradable groundwater permits in two European contexts
Practical issues in developing a smart surface irrigation system with real-time simulation of furrow advance

Automation of surface irrigation can be an economic and ecological way of increasing global food production. In this work a fully automated cablegation system is evaluated that adapts the application time and depth to the actual infiltration rate of the soil in real-time. The system calculates the infiltration equation from advance times in a control furrow and then simulates irrigation in every furrow of the field, establishing the optimum application time for each furrow. The methodology was evaluated in a field organized in contour terraces with furrows of various lengths in order to evaluate practical issues affecting the performance of the system. The results confirm the temporal variability in soil infiltration, and the need for real-time determination of the infiltration equation. The evolution of furrow geometry through the season did not have an important impact on the results of the simulations. The length of the furrow considered for calculating the Kostiakov equation influences the parameters of the equation. Automation with real-time feedback can result in important savings in water and labour and can produce irrigation events with more than 90% application efficiency. Nevertheless, the results also indicate that there are practical limits to what can possibly be achieved with automation and real-time feedback from the fiel