Combining Multiple Classifiers with Dynamic Weighted Voting

When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In this paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results demonstrate the advantages of these novel strategies over the simple voting scheme

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

Improving image classification in a complex wetland ecosystem through image fusion techniques

The aim of this study was to evaluate the impact of image fusion techniques on vegetation classification accuracies in a complex wetland system. Fusion of panchromatic (PAN) and multispectral (MS) Quickbird satellite imagery was undertaken using four image fusion techniques: Brovey, hue-saturation-value (HSV), principal components (PC), and Gram– Schmidt (GS) spectral sharpening. These four fusion techniques were compared in terms of their mapping accuracy to a normal MS image using maximum-likelihood classification(MLC) and support vector machine (SVM) methods. Gram–Schmidt fusion technique yielded the highest overall accuracy and kappa value with both MLC (67.5% and 0.63, respectively) and SVM methods (73.3% and 0.68, respectively). This compared favorably with the accuracies achieved using the MS image. Overall, improvements of 4.1%, 3.6%, 5.8%, 5.4%, and 7.2% in overall accuracies were obtained in case of SVM over MLC for Brovey, HSV, GS, PC, and MS images, respectively. Visual and statistical analyses of the fused images showed that the Gram–Schmidt spectral sharpening technique preserved spectral quality much better than the principal component, Brovey, and HSV fused images. Other factors, such as the growth stage of species and the presence of extensive background water in many parts of the study area, had an impact on classification accuracies

Machine learning approximation techniques using dual trees

This master thesis explores a dual-tree framework as applied to a particular class of machine learning problems that are collectively referred to as generalized n-body problems. It builds a new algorithm on top of it and improves existing Boosted OGE classifier

Complex-Wavelet Structural Similarity Based Image Classification

Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in image classification problems has not been deeply investi- gated. In this study, we introduce a series of novel image classification algorithms based on CW-SSIM and use handwritten digit and face image recognition as examples for demonstration, including CW-SSIM based nearest neighbor method, CW-SSIM based k means method, CW-SSIM based support vector machine method (SVM) and CW-SSIM based SVM using affinity propagation. Among the proposed approaches, the best compromise between accuracy and complexity is obtained by the CW-SSIM support vector machine algorithm, which combines an unsupervised clustering method to divide the training images into clusters with representative images and a supervised learning method based on support vector machines to maximize the classification accuracy. Our experiments show that such a conceptually simple image classification method, which does not involve any registration, intensity normalization or sophisticated feature extraction processes, and does not rely on any modeling of the image patterns or distortion processes, achieves competitive performance with reduced computational cost

Design of Interactive Feature Space Construction Protocol

Machine learning deals with designing systems that learn from data i.e. automatically improve with experience. Systems gain experience by detecting patterns or regularities and using them for making predictions. These predictions are based on the properties that the system learns from the data. Thus when we say a machine learns, it means it has changed in a way that allows it to perform more efficiently than before. Machine learning is emerging as an important technology for solving a number of applications involving natural language processing applications, medical diagnosis, game playing or financial applications. Wide variety of machine learning approaches have been developed and used for a number of applications. We first review the work done in the field of machine learning and analyze various concepts about machine learning that are applicable to the work presented in this thesis. Next we examine active machine learning for pipelining of an important natural language application i.e. information extraction, in which the task of prediction is carried out in different stages and the output of each stage serves as an input to the next stage. A number of machine learning algorithms have been developed for different applications. However no single machine learning algorithm can be used appropriately for all learning problems. It is not possible to create a general learner for all problems because there are varied types of real world datasets that cannot be handled by a single learner. For this purpose an evaluation of the machine learning algorithms is needed. We present an experiment for the evaluation of various state-of-the-art machine learning algorithms using an interactive machine learning tool called WEKA (Waikato Environment for Knowledge Analysis). Evaluation is carried out with the purpose of finding an optimal solution for a real world learning problemcredit approval used in banks. It is a classification problem. Finally, we present an approach of combining various learners with the aim of increasing their efficiency. We present two experiments that evaluate the machine learning algorithms for efficiency and compare their performance with the new combined approach, for the same classification problem. Later we show the effects of feature selection on the efficiency of our combined approach as well as on other machine learning techniques. The aim of this work is to analyze the techniques that increase the efficiency of the learners