Accurate and Resource-Aware Classification Based on Measurement Data (original) (raw)
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The concept of multiobjective optimization (MOO) has been integrated with variable length chromosomes for the development of a nonparametric genetic classifier which can overcome the problems, like overfitting/overlearning and ignoring smaller classes, as faced by single objective classifiers. The classifier can efficiently approximate any kind of linear and/or nonlinear class boundaries of a data set using an appropriate number of hyperplanes. While designing the classifier the aim is to simultaneously minimize the number of misclassified training points and the number of hyperplanes, and to maximize the product of class wise recognition scores. The concepts of validation set (in addition to training and test sets) and validation functional are introduced in the multiobjective classifier for selecting a solution from a set of nondominated solutions provided by the MOO algorithm. This genetic classifier incorporates elitism and some domain specific constraints in the search process, and is called the CEMOGA-Classifier (constrained elitist multiobjective genetic algorithm based classifier). Two new quantitative indices, namely, the purity and minimal spacing, are developed for evaluating the performance of different MOO techniques. These are used, along with classification accuracy, required number of hyperplanes and the computation time, to compare the CEMOGA-Classifier with other related ones.
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Finding a good classifier is a multiobjective optimization problem with different error rates and the costs to be minimized. The receiver operating characteristic is widely used in the machine learning community to analyze the performance of parametric classifiers or sets of Pareto optimal classifiers. In order to directly compare two sets of classifiers the area (or volume) under the convex hull can be used as a scalar indicator for the performance of a set of classifiers in receiver operating characteristic space. Recently, the convex hull based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems. The contribution of this paper is to extend this algorithm for dealing with higher dimensional problem formulations. In particular, we discuss problems where parsimony (or classifier complexity) is stated as a third objective and multi-class classification with three different true classification rates to be maximized. The design of the algorithm proposed in this paper is inspired by indicator-based evolutionary algorithms, where first a performance indicator for a solution set is established and then a selection operator is designed that complies with the performance indicator. In this case, the performance indicator will be the volume under the convex hull. The algorithm is tested and analyzed in a proof of concept study on different benchmarks that are designed for measuring its capability to capture relevant parts of a convex hull. Further benchmark and application studies on email classification and feature selection round up the analysis and assess robustness and usefulness of the new algorithm in real world settings.
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm
Finding a good classifier is a multiobjective optimization problem with different error rates and the costs to be minimized. The receiver operating characteristic is widely used in the machine learning community to analyze the performance of parametric classifiers or sets of Pareto optimal classifiers. In order to directly compare two sets of classifiers the area (or volume) under the convex hull can be used as a scalar indicator for the performance of a set of classifiers in receiver operating characteristic space.
A Flexible Classifier Design Framework Based on Multiobjective Programming
IEEE Transactions on Audio, Speech & Language Processing, 2008
We propose a multi-objective programming (MOP) framework for finding compromise solutions that are satisfactory for each of multiple competing performance criteria in a pattern classification task. The fundamental idea for our formulation of classifier learning, which we refer to as iterative constrained optimization (ICO), evolves around improving one objective while allowing the rest to degrade. This is achieved by the optimization of individual objectives with proper constraints on the remaining competing objectives. The constraint bounds are adjusted based on the objective functions obtained in the most recent iteration. An aggregated utility function is used to evaluate the acceptability of local changes in competing criteria, i.e., changes from one iteration to the next. Although many MOP approaches developed so far are formal and extensible to large number of competing objectives, their capabilities are examined only with two or three objectives. This is mainly because practical problems become significantly harder to manage when the number of objectives gets larger. We, however, illustrate the proposed framework in the context of automatic language identification (LID) of 12 languages and 3 dialects. This LID task requires the simultaneous minimization of the false-acceptance and false-rejection rates for each of the 15 languages/dialects, and, hence, is an MOP problem with a total of 30 competing objectives. In our experiments, we observed that the ICO-trained classifiers result in not only reduced error rates but also a good balance among the many competing objectives when compared to those classifiers that minimize an overall objective. We interpret our experimental findings as evidence for ICO offering a greater degree of freedom for classifier design.
Artificial Intelligence Review, 2017
The Support Vector Machines (SVM) constitute a very powerful technique for pattern classification problems. However, its efficiency in practice depends highly on the selection of the kernel function type and relevant parameter values. Selecting relevant features is another factor that can also impact the performance of SVM. The identification of the best set of parameters values for a classification model such as SVM is considered as an optimization problem. Thus, in this paper, we aim to simultaneously optimize SVMs parameters and feature subset using different kernel functions. We cast this problem as a multi-objective optimization problem, where the classification accuracy, the number of support vectors, the margin and the number of selected features define our objective functions. To solve this optimization problem, a method based on multi-objective genetic algorithm NSGA-II is suggested. A multi-criteria selection operator for our NSGA-II is also introduced. The proposed method is tested on some benchmark data-sets. The experimental results show the efficiency of the proposed method where features were reduced and the classification accuracy has been improved.