An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL) strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED). Differential evolution (DE) is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE) is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images

Optimized classification predictions with a new index combining machine learning algorithms

Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search

Reconstruction and motion estimation of sparsely sampled ionospheric data

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New rule induction algorithms with improved noise tolerance and scalability

As data storage capacities continue to increase due to rapid advances in information technology, there is a growing need for devising scalable data mining algorithms able to sift through large volumes of data in a short amount of time. Moreover, real-world data is inherently imperfect due to the presence of noise as opposed to artificially prepared data. Consequently, there is also a need for designing robust algorithms capable of handling noise, so that the discovered patterns are reliable with good predictive performance on future data. This has led to ongoing research in the field of data mining, intended to develop algorithms that are scalable as well as robust. The most straightforward approach for handling the issue of scalability is to develop efficient algorithms that can process large datasets in a relatively short time. Efficiency may be achieved by employing suitable rule mining constraints that can drastically cut down the search space. The first part of this thesis focuses on the improvement of a state-of-the-art rule induction algorithm, RULES-6, which incorporates certain search space pruning constraints in order to scale to large datasets. However, the constraints are insufficient and also have not been exploited to the maximum, resulting in the generation of specific rules which not only increases learning time but also the length of the rule set. In order to address these issues, a new algorithm RULES-7 is proposed which uses deep rule mining constraints from association learning. This results in a significant drop in execution time for large datasets while boosting the classification accuracy of the model on future data. A novel comparison heuristic is also proposed for the algorithm which improves classification accuracy while maintaining the execution time. Since an overwhelming majority of induction algorithms are unable to handle the continuous data ubiquitous in the real-world, it is also necessary to develop an efficient discretisation procedure whereby continuous attributes can be treated as discrete. By generalizing the raw continuous data, discretisation helps to speed up the induction process and results in a simpler and intelligible model that is also more accurate on future data. Many preprocessing discretisation techniques have been proposed to date, of which the entropy based technique has by far been accepted as the most accurate. However, the technique is suboptimal for classification because of failing to identify the cut points within the value range of each class for a continuous attribute, which deteriorates its classification accuracy. The second part of this thesis presents a new discretisation technique which utilizes the entropy based principle but takes a class-centered approach to discretisation. The proposed technique not only increases the efficiency of rule induction but also improves the classification accuracy of the induced model. Another issue with existing induction algorithms relates to the way covered examples are dealt with when a new rule is formed. To avoid problems such as fragmentation and small disjuncts, the RULES family of algorithms marks the examples instead of removing them. This tends to increase overlapping between rules. The third part of this thesis proposes a new hybrid pruning technique in order to address the overlapping issue so as to reduce the rule set size. It also proposes an incremental post-pruning technique designed specifically to handle the issue of noisy data. This leads to improved induction performance as well as better classification accuracy