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In this paper, a new Hierarchical fuzzy classifier based on evolutionary boosting algorithms is proposed. The main goal of this paper is to improve the performance of fuzzy rule based classifiers through utilizing hierarchical structure for achieving fuzzy rules. The advantages of hierarchical fuzzy rules generated by evolutionary boosting algorithms are evaluated by comparison between the performance...
In this paper we present a new approach for classification of microarray data. Our methodology consists of two steps: an attribute selection, which aims at selection of the most informative genes, and a classification of expression profiles, which is carried out by weighted voting, a novel instance-based classifier based on Rough Set Theory. Attribute selection consists of two stages - initial selection,...
In this paper, a modified XCS is proposed to reduce the numbers of learned rules. XCS is a type of learning classifier systems and has been proven able to find accurate, maximal generalizations. However, XCS usually produces too many rules such that the readability of the classification model is greatly reduced. As a result, XCS users may not be able to obtain the desired knowledge or useful information...
This study proposes a novel classification technique of GA/k-prototypes in combination with a genetic algorithm to take the advantage of k-prototypes clustering mechanism for supporting the classification purpose. A genetic algorithm is used to adjust the weight applied to input attributes in order to enable a majority of the data records in each cluster to be with the same outcome class. We conduct...
Recently, it has been shown that, in ensemble learning, it may be preferable to ensemble some instead of all the classifiers. Various selective ensemble approaches are then designed, where optimization algorithms like genetic algorithm (GA) are used to evolve weights of component classifiers and classifiers with weights greater than a threshold are selected. This paper proposes a novel selective ensemble...
Accurate software effort estimation is essential for successful project management. To improve the accuracy, a number of estimation techniques have been developed. Among those, Analogy-Based Estimation (ABE) has become one of the mainstreams of effort estimation. In general, ABE infers the effort to accomplish a new project from the efforts of the historical projects which possess similar characteristics...
In the last years, the area of Multicriteria Decision Analysis (MCDA) has brought about new methods to cope with classification problems, among which those based on the concept of prototypes. These refer to specific alternatives (samples) of the training dataset that are good representatives of the groups they fit in. In this paper, experiments are conducted over two prototype selection (PS) techniques...
Accurate classification of caller interactions within Interactive Voice Response systems would assist corporations to determine caller behavior within these telephony applications. This paper details the development of such a classification system for a pay beneficiary application. Fuzzy Inference Systems, Multi-Layer Perceptron, Support Vector Machine and ensemble of classifiers were developed. Accuracy,...
In this paper, we present a new approach which combines particle swarm optimization (PSO) with ensemble techniques to study credit risk assessment problems. In each iteration of the proposed method, PSO is used to solve feature subset selection problems and then nearest neighbor classifiers classify credit risk. Finally, all individual classification outputs are combined to generate the final aggregated...
Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, increasing SVM classification accuracy. The study focuses...
In this paper, we apply an evolutionary optimization classifier, referred to as genetic algorithm-based multiple classifier (GaMC), to the long-range contacts prediction. As a result, about 44.1% contacts between long-range residues (with a sequence separation of at least 24 amino acids) are founded around the sequence profile (SP) centre when evaluating the top L/5 (L is the sequence length of protein)...
A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI...
It is interesting to discover exceptions, as they dispute the existing knowledge and have elements of unexpectedness and surprise. As exceptions focus on a very small portion of data, discovering exceptions still remains a great challenge. A censored production rule (CPR) is a special kind of knowledge structure that augments exceptions to their corresponding commonsense rules of high generality and...
Human often wants to listen to music that fits best his current emotion. A grasp of emotions in songs might be a great help for us to effectively discover music. In this paper, we aimed at automatically classifying moods of songs based on lyrics and metadata, and proposed several methods for supervised learning of classifiers. In future, we plan to use automatically identified moods of songs as metadata...
For feature subset optimization problems in text categorization, the GATS strategy which combines the genetic algorithm with taboo search algorithm is proposed in this paper to be applied to text categorization to realize the dimensionality reduction of the feature space. The experiments show that the application of this method to select the characteristics of the text can not only maintain the advantages...
When dealing with high-dimensional datasets with fewer samples, feature selection and ensemble learning are two effective strategies. In this paper, we focus our attention on genetic algorithm based feature selection for ensemble learning. We use an improved GA algorithm (IGA) to reduce the dimensionality of the feature space, and then evaluate using bagging and Ada-Boost constructed by the reduced...
Thyroid gland produces thyroid hormones to help the regulation of the body's metabolism. The abnormalities of producing thyroid hormones are divided into two categories. Hypothyroidism which is related to production of insufficient thyroid hormone and hyperthyroidism related to production of excessive thyroid hormone. Separating these two diseases is very important for thyroid diagnosis. Therefore...
In this study, a potential support vector machines (P- SVM) with genetic algorithm (GA) is proposed to supplier selection. P-SVM is used to select optimal classifier using "support feature" by exchanging the roles of data points and features. On the other hand, one-against-one method is applied to solve the problem of multi-class. In addition, genetic algorithm is applied to accomplish the...
Support vector machines (SVMs), a powerful machine method proposed by Vapnik have made significant achievement in pattern classification and regression estimation. In practice, when we use the method of SVM there exist two problems: the selection of kernel and the selection of parameters. In this paper, we discuss the hybrid kernels and propose a new method to select parameters, which uses genetic...
Gene expression data usually contains a large number of genes (several thousand or more) but a small number of samples (usually <100). Among all the genes, many are irrelevant, insignificant or redundant to the discriminant problem under investigation. Hence the identification of informative genes, which have the greatest power for classification, is of fundamental and practical importance to the...
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