International Journal of Computational Intelligence and Applications

Nowadays, documents are increasingly associated with multi-level category hierarchies rather than a flat category scheme. As the volume and diversity of documents grow, so do the size and complexity of the corresponding category hierarchies. To be able to access such hierarchically classified documents in real-time, we need fast automatic methods to navigate these hierarchies. Today’s data domains are also very different from each other, such as medicine and politics. These distinct domains can be handled by different classifiers. A document representation system which incorporates the inherent category structure of the data should also add useful semantic content to the data vectors and thus lead to better separability of classes. In this paper, we present a scalable meta-classifier to tackle today’s problem of multi-level data classification in the presence of large datasets. To speed up the classification process, we use a search-based method to detect the level-1 category of a test document. For this purpose, we use a category–hierarchy-based vector representation. We evaluate the meta-classifier by scaling to both longer documents as well as to a larger category set and show it to be robust in both cases. We test the architecture of our meta-classifier using six different base classifiers (Random forest, C4.5, multilayer perceptron, naïve Bayes, BayesNet (BN) and PART). We observe that even though there is a very small variation in the performance of different architectures, all of them perform much better than the corresponding single baseline classifiers. We conclude that there is substantial potential in this meta-classifier architecture, rather than the classifiers themselves, which successfully improves classification performance.

In this paper, we present an approach for sample selection using an ensemble of neural networks for credit scoring. The ensemble determines samples that can be considered outliers by checking the classification accuracy of the neural networks on the original training data samples. Those samples that are consistently misclassified by the neural networks in the ensemble are removed from the training dataset. The remaining data samples are then used to train and prune another neural network for rule extraction. Our experimental results on publicly available benchmark credit scoring datasets show that by eliminating the outliers, we obtain neural networks with higher predictive accuracy and simpler in structure compared to the networks that are trained with the original dataset. A rule extraction algorithm is applied to generate comprehensible rules from the neural networks. The extracted rules are more concise than the rules generated from networks that have been trained using the original datasets.

The aim of direct marketing is to find the right customers who are most likely to respond to marketing campaign messages. In order to detect which customers are most valuable, response modeling is used to classify customers as respondent or non-respondent using their purchase history information or other behavioral characteristics. Data mining techniques, including effective classification methods, can be used to predict responsive customers. However, the inherent problem of imbalanced data in response modeling brings some difficulties into response prediction. As a result, the prediction models will be biased towards non-respondent customers. Another problem is that single models cannot provide the desired high accuracy due to their internal limitations. In this paper, we propose an ensemble classification method which removes imbalance in the data, using a combination of clustering and under-sampling. The predictions of multiple classifiers are combined in order to achieve better results. Using data from a bank’s marketing campaigns, this ensemble method is implemented on different classification techniques and the results are evaluated. We also evaluate the performance of this ensemble method against two alternative ensembles. The experimental results demonstrate that our proposed method can improve the performance of the response models for bank direct marketing by raising prediction accuracy and increasing response rate.

Associative learning plays a major role in the formation of the internal dynamic engine of an adaptive system or a cognitive robot. Interaction with the environment can provide a sparse and discrete set of sample correlations of input–output incidences. These incidences of associative data points can provide useful hints for capturing underlying mechanisms that govern the system’s behavioral dynamics. In many approaches to solving this problem, of learning system’s input–output relation, a set of previously prepared data points need to be presented to the learning mechanism, as a training data, before a useful estimations can be obtained. Besides data-coding is usually based on symbolic or nonimplicit representation schemes. In this paper, we propose an incremental learning mechanism that can bootstrap from a state of complete ignorance of any representative sample associations. Besides, the proposed system provides a novel mechanism for data representation in nonlinear manner through the fusion of self-organizing maps and Gaussian receptive fields. Our architecture is based solely on cortically-inspired techniques of coding and learning as: Hebbian plasticity and adaptive populations of neural circuitry for stimuli representation.

We define a neural network that captures the problem’s data space components using emergent arrangement of receptive field neurons that self-organize incrementally in response to sparse experiences of system–environment interactions. These learned components are correlated using a process of Hebbian plasticity that relates major components of input space to those of the output space. The viability of the proposed mechanism is demonstrated through multiple experimental setups from real-world regression and robotic arm sensory-motor learning problems.

This papers deals with the design of an efficient intelligent tool for automotive engine coldstart monitoring applications. The real-time identification and control of engine coldstart operations have been proven to be very formidable tasks. This refers to the highly nonlinear and transient behavior of the engine system over coldstart operations. As the catalyst temperature is not sufficiently high, the amount of tailpipe hydrocarbon emissions is remarkable over this period. The researchers of systems sciences have investigated the development of soft sensors which are needed to monitor the catalyst temperature for enabling effective coldstart controllers to reduce the emissions. However, most of the conducted researches have focused on using complicated statistical models as well as gradient-based neural networks for the considered problem. This raises several problems regarding the generalization and computational efficiency of the proposed models. In this paper, the authors propose a novel computationally efficient method based on the integration of accelerated kernels and Tikhonov regularized extreme learning machine for the online monitoring of the catalyst temperature over the coldstart period for a given engine. Based on the results of comparative simulations, the authors demonstrate that the proposed soft sensor can be very effective for automotive coldstart applications.

Many infrastructures, such as those of finance and banking, transportation, military and telecommunications, are highly dependent on the Internet. However, as the Internet’s underlying structural protocols and governance can be disturbed by intruders, for its smooth operation, it is important to minimize such disturbances. Of the available techniques for achieving this, computational intelligence methodologies, such as evolutionary algorithms and swarm intelligence approaches, are popular and have been successfully applied to detect intrusions. In this paper, we present an overview of these techniques and related literature on intrusion detection, analyze their research contributions, compare their approaches and discuss new research directions which will provide useful insights for intrusion detection researchers and practitioners.