Interoperable intelligent environmental decision support systems: a framework proposal

In this paper, an approach for the development of Interoperable Intelligent Environmental Decision Support Systems (IEDSS) is proposed. The framework is based upon the cognitive-oriented approach for the development of IEDSS proposed in (Sànchez-Marrè et al., 2008), where three kind of tasks must be built: analysis tasks, synthesis tasks and prognosis tasks. Now, a fourth level will be proposed: the model construction layer, which is normally an off-line task. At each level, interoperability should be possible and inter-level interoperability must be als o achieved. This interoperability is proposed to be obtained using data interchange protocols like Predictive Model Markup Language (PMML), which is a model interc hange protocol based on XML language, using an ontology of data and AI models to characterize data types and AI models and to set-up a common terminology, and using workflows of the whole interoperation scheme. In the future, a Multi-Agent System will be used to implement the software components. An example of use of the pro posed methodology applied to the supervision of a Wastewater Treatment Plant is provided. This Interoperable IEDSS framework will be the first step to an actual interoperability of AI models which will make IEDSS more reliable and accurate to solve complex environmental problems.Peer ReviewedPostprint (published version

Using contextual information in music playlist recommendations

Recommender Systems have become a fundamental part of various applications supporting users when searching for items they could be interested in,at a given moment. However, the majority of Recommender Systems generate isolate item recommendations based mainly on user-item interactions, without taking into account other important information about the recommendation moment, able to deliver users a more complete experience. In this paper, a hybrid Case-based Reasoning model generating recommendations of sets of music items, based on the underlying structures found in previous playlists, is proposed. Furthermore, the described system takes into account the similarity of the basic contextual information of the current and the past recommendation moments. The initial evaluation shows that the proposed approach may deliver recommendations of equal and higher accuracy than some of the widely used techniques.Peer ReviewedPostprint (author's final draft

Using contextual information in music playlist recommendations

Recommender Systems have become a fundamental part of various applications supporting users when searching for items they could be interested in, at a given moment. However, the majority of Recommender Systems generate isolate item recommendations based mainly on user-item interactions, without taking into account other important information about the recommendation moment, able to deliver users a more complete experience. In this paper, a hybrid Case-based Reasoning model generating recommendations of sets of music items, based on the underlying structures found in previous playlists, is proposed. Furthermore, the described system takes into account the similarity of the basic contextual information of the current and the past recommendation moments. The initial evaluation shows that the proposed approach may deliver recommendations of equal and higher accuracy than some of the widely used techniquePeer ReviewedPostprint (author's final draft

Dynamic learning of cases from data streams

This paper presents a dynamic adaptive framework for building a case library being able to cope with a data stream in the field of Case-Based Reasoning. The framework provides a three-layer architecture formed by a set of case libraries dynamically built. This Dynamic and Adaptive Case Library (DACL), can process in an incremental way a data stream, and can be used as a classification model or a regression model, depending on the predicted variable. In this paper, the work is focused on classification tasks. Each case library has a first layer formed by the dynamic clusters of cases, a second one formed by the meta-cases or prototypes of the cluster, and a third one formed by an incremental indexing structure. In our approach, some variant of k-d tres have been used, in addition to an exploration technique to get a more efficient retrieval time. This three-layer famework can be constructed in an incremental way. Several meta-case learning approaches are proposed, as well as some case learning strategies. The framework has been tested with several datasets. The experimental results show a very good performance in comparison with a batch learning scheme over the same data.Peer ReviewedPostprint (author's final draft

Providing intelligent decision support systems with flexible data-intensive case-based reasoning

In this paper we present a flexible CBR shell for Data-Intensive Case-Based Reasoning Systems which is fully integrated in an Intelligent Data Analysis Tool entitled GESCONDA. The main subgoal of the developed tool is to create a CBR Shell where no fixed domain exists and where letting the expert/user creates (models) his/her own domain. From an abstract point of view, the definition of the CBR can be seen as a methodology composed by four phases and each phase offers different ways to be solved. Then, since the CBR shell is integrated in GESCONDA, it inherits all its functionalities which cover the whole knowledge discovery and data mining process and also, CBR can complement its phases with this functionality. As a result, GESCONDA becomes an intelligent decision support tool which encompasses a number of advantages including domain independence, incremental learning, platform independence and generality.Peer ReviewedPostprint (published version

Environmental data stream mining through a case-based stochastic learning approach

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Environmental data stream mining is an open challenge for Data Science. Common methods used are static because they analyze a static set of data, and provide static data-driven models. Environmental systems are dynamic and generate a continuous data stream. Dynamic methods coping with the temporal nature of data must be provided in Data Science. Our proposal is to model each environmental information unit, timely generated, as a new case/experience in a Case-Based Reasoning (CBR) system. This contribution aims to incrementally build and manage a Dynamic Adaptive Case Library (DACL). In this paper, a stochastic method for the learning of new cases and management of prototypes to create and manage the DACL in an incremental way is introduced. This stochastic method works with two main moments. An evaluation of the method has been carried using a data stream of air quality of the city of Obregon, Sonora. México, with good results. In addition, other datasets have been mined to ensure the generality of the approach.Peer ReviewedPostprint (author's final draft

Feature-tree labeling for case base maintenance

Case Base Maintenance (CBM) algorithms update the content of the case base with the aim of improving the case-based reasoner performance. In this paper, we introduce a novel CBM method called Feature-Tree Labeling (FTL) with the focus on increasing the general accuracy of a Case-Based Reasoning (CBR) system. The proposed FTL algorithm is designed to detect and remove noisy cases from the case base, based on value distribution of individual features in the available data. The competence of the FTL method has been compared with well-known state-ofthe-art CBM algorithms. The tests have been done on 25 datasets selected from the UCI repository. The results show that FTL obtains higher accuracy than some of the state-of-the-art methods and CBR, with a statistically significant degreePeer ReviewedPostprint (author's final draft

A survey on pre-processing techniques: relevant issues in the context of environmental data mining

One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.Peer ReviewedPostprint (author's final draft

Reputation-based maintenance in case-based reasoning

Case Base Maintenance algorithms update the contents of a case base in order to improve case-based reasoner performance. In this paper, we introduce a new case base maintenance method called Reputation-Based Maintenance (RBM) with the aim of increasing the classification accuracy of a Case-Based Reasoning system while reducing the size of its case base. The proposed RBM algorithm calculates a case property called Reputationfor each member of the case base, the value of which reflects the competence of the related case. Based on this case property, several removal policies and maintenance methods have been designed, each focusing on different aspects of the case base maintenance. The performance of the RBM method was compared with well-known state-of-the-art algorithms. The tests were performed on 30 datasets selected from the UCI repository. The results show that the RBM method in all its variations achieves greater accuracy than a baseline CBR, while some variations significantly outperform the state-of-the-art methods. We particularly highlight theRBM_ACBR algorithm, which achieves the highest accuracy among the methods in the comparison to a statistically significant degree, and the RBMcr algorithm, which increases the baseline accuracy while removing, on average, over half of the case basehis work has been partially supported by the SpanishMinistry of Science and Innovation with project MISMIS-LANGUAGE (grantnumber PGC2018-096212-B-C33), by the Catalan Agency of University andResearch Grants Management (AGAUR) (grants number 2017 SGR 341 and 2017SGR 574), by Spanish Network ‘‘Learning Machines for Singular Problems andApplications (MAPAS)’’ (TIN2017-90567-REDT, MINECO/FEDER EU) and by theEuropean Union’s Horizon 2020 research and innovation programme under theMarie Sklodowska-Curie grant agreement No. 860843Peer ReviewedPostprint (author's final draft

A hybrid recommender system for industrial symbiotic networks

Various solutions enabling the realization of synergies in Industrial Symbiotic Networks have been proposed. However, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find possible candidates able to cover their needs, is still of high importance. Usually, the actors participating in these networks act based on previously predefined patterns, without taking into account all the possible alternatives, usually due to the difficulty of finding and properly evaluating them. Therefore, the recommendation of new matches that the users were not aware of is a big challenge, as companies many times are not willing to change their established workflows if they are not sure about the outcome. Thus, the ability of a platform to properly identify symbiotic alternatives that could provide both economic and environmental benefits for the companies, and the sector as a whole, is of high importance and delivering such recommendations is crucial. In this work, we propose a hybrid recommender system aiming to support users in properly filtering and identifying the symbiotic relationships that may provide them an improved performance. Several criteria are taken into account in order to generate, each time, the list of the most suitable solutions for the current user, at a given moment. In addition, the implemented system uses a graph-based similarity model in order to identify resource similarities while performing a hybrid case-based recommendation in order to find the optimal solutions according to more features than just the resources’ similarities.Peer ReviewedPostprint (published version