Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems.

In this paper, we present some recent developments of Multiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and random forests) are briefly described and applied to multisource data (satellite multispectral images, elevation, slope and aspect data) for landcover classification. In the second part, special attention is given to Support Vector Machines (SVM) based algorithms. In particular, the fusion of two classifiers using both spectral and the spatial information is discussed in the frame of hyperspectral remote sensing for the classification of urban areas. In all the cases, MCS provide a significant improvement of the classification accuracies. In order to address new challenges for the analysis of remote sensing data, MCS provide invaluable tools to handle situations with an ever growing complexity. Examples include extraction of multiple features from one data set, use of multi-sensor data, and complementary use of several algorithms in a decision fusion scheme.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Normally, statistical methods are used to generate rankings for genes in terms of their ability to distinguish between normal and malignant tumors from a gene expression dataset. However, different statistical methods yield different ranks for same gene and there is no universally accepted method for ranking. Therefore rank aggregation is required to find the overall ranking of the set of genes. There are various rank aggregation methods in the existing literature to integrate the rankings produced by various statistical tests. Moreover, the problem of integration of some partial rankings, containing unequal numbers of genes, is more challenging. In this article, a multiobjective genetic algorithm based rank aggregation method is proposed to integrate some partial rankings in an unbiased way. The first objective is to minimize the total distance from the reference ranking to the input rankings. For distance calculation, the Scaled Footrule Distance is used. The second objective is to minimize the standard deviation among those distances in order to avoid bias toward a particular input ranking. The proposed method is applied on some real-life microarray gene expression datasets, and the performance of it is compared with that of several existing rank aggregation techniques with respect to accuracy and the AUC (Area under ROC curve) value. Again, for real-life datasets, accuracy is plotted for visual comparison.

In this paper, we present an unsupervised approach for estimating the effectiveness of image retrieval results obtained for a given query. The proposed approach does not require any training procedure and the computational efforts needed are very low, since only the top-k results are analyzed. In addition, we also discuss the use of the unsupervised measures in two novel rank aggregation methods, which assign weights to ranked lists according to their effectiveness estimation. An experimental evaluation was conducted considering different datasets and various image descriptors. Experimental results demonstrate the capacity of the proposed measures in correctly estimating the effectiveness of different queries in an unsupervised manner. The linear correlation between the proposed and widely used effectiveness evaluation measures achieves scores up to 0.86 for some descriptors.

PurposeMultiple classifier systems have been used widely in computing, communications, and informatics. Combining multiple classifier systems (MCS) has been shown to outperform a single classifier system. It has been demonstrated that improvement in ensemble performance depends on either the diversity among or the performance of individual systems. A variety of diversity measures and ensemble methods have been proposed and studied. However, it remains a challenging problem to estimate the ensemble performance in terms of the performance of and the diversity among individual systems. The purpose of this paper is to study the general problem of estimating ensemble performance for various combination methods using the concept of a performance distribution pattern (PDP).Design/methodology/approachIn particular, the paper establishes upper and lower bounds for majority voting ensemble performance with disagreement diversity measure Dis, weighted majority voting performance in terms of weighted average performance and weighted disagreement diversity, and plurality voting ensemble performance with entropy diversity measure D.FindingsBounds for these three cases are shown to be tight using the PDP for the input set.Originality/valueAs a consequence of the authors' previous results on diversity equivalence, the results of majority voting ensemble performance can be extended to several other diversity measures. Moreover, the paper showed in the case of majority voting ensemble performance that when the average of individual systems performance P is big enough, the ensemble performance Pm resulting from a maximum (information‐theoretic) entropy PDP is an increasing function with respect to the disagreement diversity Dis. Eight experiments using data sets from various application domains are conducted to demonstrate the complexity, richness, and diverseness of the problem in estimating the ensemble performance.

Rank aggregation is the process of aggregating multiple base rankers into a single but more comprehensive ranker, which plays an important role in many domains such as recommender system, meta-search, database, genomics, etc. Works related to the comparison of rank aggregation methods all don't have a suitable and general data generation mechanism to produce data with various characteristics and lack a more reasonable and effective algorithm evaluation performance index.Therefore, this paper presents a general data generation mechanism based on Mallows model to produce synthetic controllable datasets, uses generalized Kendall rank correlation coefficient and rank-biased overlap to evaluate and compare the performance of two kinds of methods under different settings.Besides, we also consider the comparison between indices and the impact of data characteristics on the algorithms.This paper may be helpful to researchers and decision-makers from multiple domains.

Metasearch engine is a system that provides unified access to multiple existing search engines. After the results returned from all used component search engines are collected, the metasearch system merges the results into a single ranked list which is expected to be better than the results of the best of the participating search systems. The success of a metasearch engine depends mainly on their rank aggregation method. The system is a better one, if the aggregated list of results displayed before the user satisfies the user with his information need. In this paper, we discuss the development of a metasearch engine that performs user feedback based metasearching using modified rough set based aggregation. Metasearching using the modified rough set based aggregation is performed in two phases namely the ranking rule learning phase and the rank aggregation phase. For each query in the training set, we mine the ranking rules and select the best rules-set by performing cross-validation test. Once the system is trained, we use the best rule set to get the overall ranking for the results returned from different search systems in response to other queries. We also present few snapshots of our system.

We study the problem of combining active learning suggestions to identify informative training examples by empirically comparing methods on benchmark datasets. Many active learning heuristics for classification problems have been proposed to help us pick which instance to annotate next. But what is the optimal heuristic for a particular source of data? Motivated by the success of methods that combine predictors, we combine active learners with bandit algorithms and rank aggregation methods. We demonstrate that a combination of active learners outperforms passive learning in large benchmark datasets and removes the need to pick a particular active learner a priori. We discuss challenges to finding good rewards for bandit approaches and show that rank aggregation performs well.

Rank aggregation has recently become a common approach for combining individual rankings into a consensus and for quantifying and improving performance in various applications, such as elections, web page rankings, and sports. During the past few years, rankings from many sources have become increasingly high-dimensional and partial. In this study, we develop a rank aggregation method by constructing a directed weighted competition graph. We introduce the concept of “ratio of out- and in-degrees (ROID)” to transform high-dimensional partial rankings into a single consensus. Moreover, we provide a novel effectiveness measure for the aggregate ranking according to its deviations from the ground truth ranking. The proposed method is compared with four typical methods with synthetic rankings. The results indicate that our method outperforms the other four by a significant margin and can be particularly efficient in aggregating high-dimensional rankings. The empirical results validate the effectiveness and feasibility of our method.

Summary In recent years there has been a growing interest in the development of super-vised classification techniques with higher classification reliability of satelliteimages. The superiority of one technique over the others cannot be claimed.Many experimental results showed that the classification accuracy dependsmore on the particular application than on the technique chosen to performthe task. Moreover in many applications it is very difficult to design a clas-sification system that exhibits the required accuracy for the final classifica-tion product. Therefore a new technique is emerging that considers multipleclassifier systems (MCSs) instead of a single classification technique. Neuralnetworks can participate effectively in a MCS in several ways. This chapterfocuses on the role of neural networks in MCSs, which can be either: (1) anindividual classifier among the classifiers ensemble, (2) the fusion center thatintegrates the decisions of individual classifiers, (3) the selector that pickssome classifiers’ decisions and ignores the others, (4) or the selector and fuserat the same time.The chapter surveys key neural and non-neural methods in this area anddevelops some new ones. It also considers several architectures of neural net-works including traditional feed-forward neural networks, probabilistic neuralnetworks and radial-basis nets. All methods are evaluated on two real remote-sensing datasets, one of which is the standard benchmark Satimage dataset.

The analytic hierarchy process (AHP) is a method to help people make a complex decision by analyzing and synthesizing multiple criteria for the decision objective in a hierarchy. We adopt the AHP as a new strategy for the semantic concept detection (SCD) in video so that multiple factors involved in SCD, including multiple modalities and relating concepts, can be hierarchically analyzed and synthesized. In this paper, we first explain, by an example, why and how the SCD problem can be analyzed by the AHP. Then, following the idea of the AHP, we develop a new rank aggregation (RA) method, called AHP-RA. Experimental results of RA for SCD in video show the effectiveness of this method.

Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers

The problem of interpreting or aggregating multiple rankings is common to many real-world applications. Perhaps the simplest and most common approach is a weighted rank aggregation, wherein a (convex) weight is applied to each input ranking and then ordered. This paper describes a new tool for visualizing and displaying ranking information for the weighted rank aggregation method. Traditionally, the aim of rank aggregation is to summarize the information from the input rankings and provide one final ranking that hopefully represents a more accurate or truthful result than any one input ranking. While such an aggregated ranking is, and clearly has been, useful to many applications, it also obscures information. In this paper, we show the wealth of information that is available for the weighted rank aggregation problem due to its structure. We apply weight set decomposition to the set of convex multipliers, study the properties useful for understanding this decomposition, and visualize the indifference regions. This methodology reveals information–that is otherwise collapsed by the aggregated ranking–into a useful, interpretable, and intuitive decision support tool. Included are multiple illustrative examples, along with heuristic and exact algorithms for computing the weight set decomposition.

Geometrical and thematic data about terrain objects stored in a geographical information system (GIS) can be kept up-to-date by using remote sensing (RS) data. Geometrical and thematic data can be extracted from the RS data by segmentation and classification techniques respectively. The possibilities and reliability of the information extraction from RS data can be improved by the use of ancillary data and knowledge about the terrain objects. Object classification and aggregation hierarchies can be used to describe relationships between terrain objects; the categorization of the different types of terrain object dynamics that is presented will be based partly on these hierarchical relationships. Object models will be applied in a case study in which both the field geometry (field boundaries) and the crop type of agricultural fields are updated from a Landsat TM image. For that purpose, a three-stage strategy has been developed. In the first stage, the results of an edge detection procedure are integrated with fixed geometrical data already contained in the GIS by using knowledge about the aggregation structure and shape of the fields. In the next stage, the crop type of the fields is determined by means of object-based classification. Finally, conditional merging is performed to solve the problem of oversegmentation. The resulting field geometry was found to agree for 87% with the field geometry as determined by a photo-interpreter. >