Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

A Multi-Attribute Group Decision Approach Based on Rough Set Theory and Application in Supply Chain Partner Selection

In multi-attribute group decision, decision makers (DMs) are willing or able to provide only incomplete information because of time pressure, lack of knowledge or data, and their limited expertise related with problem domain, so the alternative sets judged by different decision makers are inconsistent in allusion to a certain decision problem, how to form consistent alternative sets becomes a very important problem. There have been a few studies considering incomplete information in group settings, but few papers consider the adjustment of inconsistent alternative sets. We suggest a method, utilizing individual decision results to form consistent alternative sets based on Rough Set theory. The method can be depicted as follows: (1) decision matrix of every decision maker is transformed to decision table through an new discretization algorithm of condition attributes ; (2) we analyze the harmony of decision table of every DM in order to filter some extra alternatives with the result that new alternative sets are formed; (3) if the new alternative sets of different DMs are inconsistent all the same, learning quality of DMs for any inconsistent alternative is a standard of accepting the alternative

Fuzzy-Rough Sets Assisted Attribute Selection

Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, attribute selectors preserve the original meaning of the attributes after reduction. This has found application in tasks that involve datasets containing huge numbers of attributes (in the order of tens of thousands) which, for some learning algorithms, might be impossible to process further. Recent examples include text processing and web content classification. AS techniques have also been applied to small and medium-sized datasets in order to locate the most informative attributes for later use. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in AS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based attribute selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This paper investigates a novel approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. FRFS is applied to two challenging domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other AS techniques in the comparative study

Going Deeper than Supervised Discretisation in Processing of Stylometric Features

Rough set theory is employed in cases where data are incomplete and inconsistent and an ap- proximation of concepts is needed. The classical approach works for discrete data and allows only nominal classification. To induce the best rules, access to all available information is ad- vantageous, which can be endangered if discretisation is a necessary step in the data preparation stage. Discretisation, even executed with taking into account class labels of instances, brings some information loss. The research methodology illustrated in this paper is dedicated to ex- tended transformations of continuous input features into categorical, with the goal of enhancing the performance of rule-based classifiers, constructed with rough set data mining. The experi- ments were carried out in the stylometry domain, with its key task of authorship attribution. The obtained results indicate that supporting supervised discretisation with elements of unsuper- vised transformations can lead to enhanced predictions, which shows the merits of the proposed research framework

Discretisation of conditions in decision rules induced for continuous

Typically discretisation procedures are implemented as a part of initial pre-processing of data, before knowledge mining is employed. It means that conclusions and observations are based on reduced data, as usually by discretisation some information is discarded. The paper presents a different approach, with taking advantage of discretisation executed after data mining. In the described study firstly decision rules were induced from real-valued features. Secondly, data sets were discretised. Using categories found for attributes, in the third step conditions included in inferred rules were translated into discrete domain. The properties and performance of rule classifiers were tested in the domain of stylometric analysis of texts, where writing styles were defined through quantitative attributes of continuous nature. The performed experiments show that the proposed processing leads to sets of rules with significantly reduced sizes while maintaining quality of predictions, and allows to test many data discretisation methods at the acceptable computational costs

A Comparison of Four Approaches to Discretization Based on Entropy †

We compare four discretization methods, all based on entropy: the original C4.5 approach to discretization, two globalized methods, known as equal interval width and equal frequency per interval, and a relatively new method for discretization called multiple scanning using the C4.5 decision tree generation system. The main objective of our research is to compare the quality of these four methods using two criteria: an error rate evaluated by ten-fold cross-validation and the size of the decision tree generated by C4.5. Our results show that multiple scanning is the best discretization method in terms of the error rate and that decision trees generated from datasets discretized by multiple scanning are simpler than decision trees generated directly by C4.5 or generated from datasets discretized by both globalized discretization methods

Multivariate discretization of continuous valued attributes.

The area of Knowledge discovery and data mining is growing rapidly. Feature Discretization is a crucial issue in Knowledge Discovery in Databases (KDD), or Data Mining because most data sets used in real world applications have features with continuously values. Discretization is performed as a preprocessing step of the data mining to make data mining techniques useful for these data sets. This thesis addresses discretization issue by proposing a multivariate discretization (MVD) algorithm. It begins withal number of common discretization algorithms like Equal width discretization, Equal frequency discretization, Naïve; Entropy based discretization, Chi square discretization, and orthogonal hyper planes. After that comparing the results achieved by the multivariate discretization (MVD) algorithm with the accuracy results of other algorithms. This thesis is divided into six chapters, covering a few common discretization algorithms and tests these algorithms on a real world datasets which varying in size and complexity, and shows how data visualization techniques will be effective in determining the degree of complexity of the given data set. We have examined the multivariate discretization (MVD) algorithm with the same data sets. After that we have classified discrete data using artificial neural network single layer perceptron and multilayer perceptron with back propagation algorithm. We have trained the Classifier using the training data set, and tested its accuracy using the testing data set. Our experiments lead to better accuracy results with some data sets and low accuracy results with other data sets, and this is subject ot the degree of data complexity then we have compared the accuracy results of multivariate discretization (MVD) algorithm with the results achieved by other discretization algorithms. We have found that multivariate discretization (MVD) algorithm produces good accuracy results in comparing with the other discretization algorithm

Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians

This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications