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  • Homage to Lofti A. Zadeh

    At IPMU2018 a well-deserved homage will be paid to his scientific work.
    Cádiz, Spain, June 11th - 15th, 2018

  • ISAPEP'21

    5th I W on Intelligent Systems for Agriculture Production and Environment Protection
    Dubai, United Arab Emirates, June 21th - 22th, 2021

  • NIP
    NIP-Software tool to manage
    low quality datasets
    © Univ. Murcia 2012
    R.P.I. nº 8/2012/700

  • FCTA 2011
    Best Student Paper Award
    "Constructing Fuzzy Partitions from Imprecise Data"
    J.M. Cadenas, M.C. Garrido, R. Martinez

  • FCTA 2012
    Best Paper Award
    "Towards an Approach to Select Features from Low Quality Datasets"
    J.M. Cadenas, M.C. Garrido, R. Martinez


Experimental studies and results with low quality datasets

We show some relevant research papers in which some of the classification datasets avalaible in Heurimind Lab web have been employed. For each study, we provide its reference (plain text and BibTeX formats) and abstract.

  Year   Experimental Studies  Link   BibTex 
2018 J.M. Cadenas, M.C. Garrido, R. Martínez, E. Muñoz, P.P. Bonissone. A fuzzy K-nearest neighbor classifier to deal with imperfect data. Soft Computing 22, 3313-3330, 2018.   doi
Abstract [▲/▼]

The k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNNimp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and real-world datasets with imperfect data. The results, validated through statistical tests, show that the kNNimp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection.

2013 J.M. Cadenas, M.C. Garrido, R. Martínez. NIP - An Imperfection Processor to Data Mining datasets. International Journal of Computational Intelligence Systems 6 (supplement 1), 3-17, 2013.   doi
Abstract [▲/▼]

Every day there are more techniques that can work with low quality data. As a result, issues related to data quality have become more crucial and have consumed a majority of the time and budget of data mining projects. One problem for researchers is the lack of low quality data in order to test their techniques with this data type. Also, as far as we know, there is no software tool focused on the create/manage low quality datasets which treats, in the widest possible way, the low quality data and helps us to create repositories with low quality datasets for testing and comparison of data mining techniques and algorithms. For this reason, we present in this paper a software tool which can create/manage low quality datasets. Among other things, the tool can transform a dataset by adding low quality data, removing and replacing any data, constructing a fuzzy partition of the attributes, etc. It also allows different input/output formats of the dataset.

2013 J.M. Cadenas, M.C. Garrido, R. Martínez. Feature subset selection Filter-Wrapper based on low quality data. Expert Systems with Applications 40(16), 6241-6252, 2013.    (Datasets used in this paper: Go to datasets)   doi
Abstract [▲/▼]

Today, feature selection is an active research in machine learning. The main idea of feature selection is to choose a subset of available features, by eliminating features with little or no predictive information, as well as redundant features that are strongly correlated. There are a lot of approaches for feature selection, but most of them can only work with crisp data. Until now there have not been many different approaches which can directly work with both crisp and low quality (imprecise and uncertain) data. That is why, we propose a new method of feature selection which can handle both crisp and low quality data. The proposed approach is based on a Fuzzy Random Forest and it integrates filter and wrapper methods into a sequential search procedure with improved classification accuracy of the features selected. This approach consists of the following main steps: (1) scaling and discretization process of the feature set; and feature pre-selection using the discretization process (filter); (2) ranking process of the feature pre-selection using the Fuzzy Decision Trees of a Fuzzy Random Forest ensemble; and (3) wrapper feature selection using a Fuzzy Random Forest ensemble based on cross-validation. The efficiency and effectiveness of this approach is proved through several experiments using both high dimensional and low quality datasets. The approach shows a good performance (not only classification accuracy, but also with respect to the number of features selected) and good behavior both with high dimensional datasets (microarray datasets) and with low quality datasets.

2012 J.M. Cadenas, M.C. Garrido, R. Martínez, P.P. Bonissone. Extending information processing in a Fuzzy Random Forest ensemble. Soft Computing 16(5), 845-861, 2012.    (Datasets used in this paper: Go to datasets)   doi
Abstract [▲/▼]

Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty, and imprecision that can be handled. In this paper, we will present a Fuzzy Random Forest ensemble for classification and show its ability to handle imperfect data into the learning and the classification phases. Then, we will describe the types of imperfect data it supports. We will devise an augmented ensemble that can operate with others type of imperfect data: crisp, missing, probabilistic uncertainty, and imprecise (fuzzy and crisp) values. Additionally, we will perform experiments with imperfect datasets created for this purpose and datasets used in other papers to show the advantage of being able to express the true nature of imperfect information.

2012 J.M. Cadenas, M.C. Garrido, R. Martínez, P.P. Bonissone. OFP_CLASS: A hybrid method to generate optimized fuzzy partitions for classification. Soft Computing 16(4), 667-682, 2012.   doi
Abstract [▲/▼]

The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means of fuzzy sets, which constitute a fuzzy partition of the domains of these attributes. This method carries out a fuzzy discretization of continuous attributes in two stages. A fuzzy decision tree is used in the first stage to propose an initial set of crisp intervals, while a genetic algorithm is used in the second stage to define the membership functions and the cardinality of the partitions. After defining the fuzzy partitions, we evaluate and compare them with previously existing ones in the literature.

2010 P.P. Bonissone, J.M. Cadenas, M.C. Garrido, R.A. Díaz-Valladares. A Fuzzy Random Forest. Int. Journal of Approximate Reasoning 51(7), 729-747, 2010.   doi
Abstract [▲/▼]

When individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman's methodology, in this paper a multiple classifier system based on a ''forest'' of fuzzy decision trees, i.e., a fuzzy random forest, is proposed. This approach combines the robustness of multiple classifier systems, the power of the randomness to increase the diversity of the trees, and the flexibility of fuzzy logic and fuzzy sets for imperfect data management. Various combination methods to obtain the final decision of the multiple classifier system are proposed and compared. Some of them are weighted combination methods which make a weighting of the decisions of the different elements of the multiple classifier system (leaves or trees). A comparative study with several datasets is made to show the efficiency of the proposed multiple classifier system and the various combination methods. The proposed multiple classifier system exhibits a good accuracy classification, comparable to that of the best classifiers when tested with conventional data sets. However, unlike other classifiers, the proposed classifier provides a similar accuracy when tested with imperfect datasets (with missing and fuzzy values) and with datasets with noise.

2010 M.C. Garrido, J.M. Cadenas, P.P. Bonissone. A Classification and Regression Technique to handle Heterogeneous and Imperfect Information. Soft Computing 14(11), 1165-1185, 2010.   doi
Abstract [▲/▼]

Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into learning and inference methods, there are many limitations as to the type of data, uncertainty and imprecision that can be handled. In this paper, we propose a classification and regression technique to handle imperfect information. We incorporate the handling of imperfect information into both the learning phase, by building the model that represents the situation under examination, and the inference phase, by using such a model. The model obtained is global and is described by a Gaussian mixture. To show the efficiency of the proposed technique, we perform a comparative study with a broad baseline of techniques available in literature tested with several data sets.

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