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Lending loans to borrowers is considered one of the main profit sources for banks and financial institutions. Thus, careful assessment and evaluation should be taken when deciding to grant credit to potential borrowers. With the rapid growth of credit industry and the massive volume of financial data, developing effective credit scoring models is very crucial. The literature in this area is very dense...
Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature,...
In real environment, the protocol distribution of Network traffic is imbalance, and the generalization ability of supervised learning algorithm such as algorithm to C4.5 is poor. In order to improve the classification accuracy and stability of network traffic, a network traffic classification method based on Rotation Forest was proposed. In the method, PCA was used for feature reduction and C4.5 algorithm...
During the last few years, imbalanced data classification issue has gained a great deal of attention. Many real life applications suffer from imbalanced distribution of data that can be handled by using different approaches such as data level, algorithm level or classifier ensembles. Single level as well as multi level classifier ensemble technique has shown improvement in classification performance...
In multiclass classification problems we face the challenge of having many binary classifiers. Consulting this large number of classifiers might be confusing and time consuming. In this paper, we propose a new framework for training and prediction in multiclass problems. In this framework, we perform traditional training. Next we map training examples to prediction models. Finally we produce the Example...
Fine-grained image categorization must handle huge cross-class ambiguities and a large number of classes. Inspired by the success of rigid hierarchical classification, we propose a new flexible hierarchical classification method, called a data-driven taxonomy forest. It constructs a multitude of taxonomies, each of which converts a complex multi-class problem to a more easily tractable path-finding...
Intrusion detection system is widely used to protect and reduce damage to information system. It protects virtual and physical computer networks against threats and vulnerabilities. Presently, machine learning techniques are widely extended to implement effective intrusion detection system. Neural network, statistical models, rule learning, and ensemble methods are some of the kinds of machine learning...
An ensemble can be defined as a set of separately trained classifiers whose predictions are combined in order to achieve better accuracy. It is proved that ensemble methods improve the performance of individual classifiers as long as the members of the ensemble are sufficiently diverse. Much research has been done using different approaches in order to obtain successful ensembles. One of the most...
Classification is a supervised learning technique typically uses two-thirds of the given annotated data set for training and the remaining for test. In this paper, we developed a frame work which uses less than one-third of the data set for training and tests the remaining two-thirds of the data and still gives results comparable to other classifiers. To achieve good classification accuracy with small...
In multiple classifier systems, base classifiers are trained to be accurate and diverse by a set of training data. The generation of training data is necessary and important in classifier ensemble, which can be achieved by instance selection (IS) or feature selection (FS) on initial data. In this paper, a feature-prior FS-IS hybrid ensemble method is proposed by integrating feature selection with...
Machine learning techniques have been earnestly explored by many software engineering researchers. At present state of art, there is no conclusive evidence on the kind of machine learning techniques which are most accurate and efficient for software defect prediction but some recent studies suggest that combining multiple machine learners, that is, ensemble learning, may be a more accurate alternative...
Indirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this...
Given multiple classifiers, one prevalent approach in classifier ensemble is to diversely combine classifier components (diversity-based ensemble), and a lot of previous works show that this approach can improve accuracy in classification. However, how to measure diversity and perform diversity-based learning are still challenges in the literature. Moreover, the learning procedure highly depends upon...
Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates...
In the Machine Learning systems several imbalanced data sets exhibit skewed class distributions in which most cases are allocated to a class and far fewer cases to a smaller one. A classifier induced from an imbalanced data set has usually a low error rate for the majority class and an unacceptable error rate for the minority class. In this paper a synoptic review of the various related methodologies...
In a preceding contribution, we proposed a novel combination method by means of a fuzzy linguistic rule-based classification system. The fuzzy linguistic combination method was based on a genetic fuzzy system in order to learn its parameters from data. By doing so the resulting classifier ensemble was able to show a hierarchical structure and the operation of the latter component was transparent to...
In this work we investigate the process of transferringthe activity recognition models of the nodes of a BodySensor Network and we proposed a methodology that supportsand makes the transferring possible. The methodology, based on acollaborative training strategy, makes use of classifier ensemblesof randomised trees that allow to generate activity recognitionmodels able to be successfully transferred...
Abstract- Concept drift has been a very important concept in the realm of data streams. Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. Concept drift occurs when a set of examples has legitimate class labels at one time and has different legitimate labels at another time. This paper provides a comprehensive overview of existing concept -evolution...
The American Cancer Society (ACS) recommends women aged 40 and above have a mammogram every year as a Gold Standard for breast cancer detection. Multiple Classifier Technique, which is a hybrid intelligent system, aims to improve the Classification accuracy rate over single classifiers. In this paper, we present an effective approach to breast mammogram analysis to modify the classification accuracy...
This paper presents an approach for finding the effect of varying hidden neurons and data size on various parameters in neural ensemble classifier. The approach is based on incrementing hidden neurons in base classifiers and training them by decrementing the training data and testing using exactly same size data. The experimental analysis of hidden neurons and data size on clusters, layers, diversity...
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