Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images

We study filter–based feature selection methods for classification of biomedical images. For feature selection, we use two filters — a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds [9], the min-redundancy max-relevance integration [8,23,36], and our Hopfield network selection. We conclude that on the whole, Hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when\ud more features are selected

Deriving Classifiers with Single and Multi-Label Rules using New Associative Classification Methods

Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number of candidate rules, and therefore reducing the classifier size so end-user can easily exploit and maintain it. Moreover, an investigation to both rule ranking and test data classification steps have been conducted in order to improve the performance of AC algorithms in regards to predictive accuracy. Overall, this thesis investigates different problems related to AC not limited to the ones listed above, and the results are new AC algorithms that devise single and multi-label rules from different applications data sets, together with comprehensive experimental results. To be exact, the first algorithm proposed named Multi-class Associative Classifier (MAC): This algorithm derives classifiers where each rule is connected with a single class from a training data set. MAC enhanced the rule discovery, rule ranking, rule filtering and classification of test data in AC. The second algorithm proposed is called Multi-label Classifier based Associative Classification (MCAC) that adds on MAC a novel rule discovery method which discovers multi-label rules from single label data without learning from parts of the training data set. These rules denote vital information ignored by most current AC algorithms which benefit both the end-user and the classifier’s predictive accuracy. Lastly, the vital problem related to web threats called “website phishing detection” was deeply investigated where a technical solution based on AC has been introduced in Chapter 6. Particularly, we were able to detect new type of knowledge and enhance the detection rate with respect to error rate using our proposed algorithms and against a large collected phishing data set. Thorough experimental tests utilising large numbers of University of California Irvine (UCI) data sets and a variety of real application data collections related to website classification and trainer timetabling problems reveal that MAC and MCAC generates better quality classifiers if compared with other AC and rule based algorithms with respect to various evaluation measures, i.e. error rate, Label-Weight, Any-Label, number of rules, etc. This is mainly due to the different improvements related to rule discovery, rule filtering, rule sorting, classification step, and more importantly the new type of knowledge associated with the proposed algorithms. Most chapters in this thesis have been disseminated or under review in journals and refereed conference proceedings

LODE: A distance-based classifier built on ensembles of positive and negative observations

International audienceCurrent work on assembling a set of local patterns such as rules and class association rules into a global model for the prediction of a target usually focuses on the identification of the minimal set of patterns that cover the training data. In this paper we present a different point of view: the model of a class has been built with the purpose to emphasise the typical features of the examples of the class. Typical features are modelled by frequent itemsets extracted from the examples and constitute a new representation space of the examples of the class. Prediction of the target class of test examples occurs by computation of the distance between the vector representing the example in the space of the itemsets of each class and the vectors representing the classes. It is interesting to observe that in the distance computation the critical contribution to the discrimination between classes is given not only by the itemsets of the class model that match the example but also by itemsets that do not match the example. These absent features constitute some pieces of information on the examples that can be considered for the prediction and should not be disregarded. Second, absent features are more abundant in the wrong classes than in the correct ones and their number increases the distance between the example vector and the negative class vectors. Furthermore, since absent features are frequent features in their respective classes, they make the prediction more robust against over-fitting and noise. The usage of features absent in the test example is a novel issue in classification: existing learners usually tend to select the best local pattern that matches the example - and do not consider the abundance of other patterns that do not match it. We demonstrate the validity of our observations and the effectiveness of LODE, our learner, by means of extensive empirical experiments in which we compare the prediction accuracy ofLODE with a consistent set of classifiers of the state of the art. In this paper we also report the methodology that we adopted in order to determine automatically the setting of the learner and of its parameters

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method

Perceptron theory can predict the accuracy of neural networks

Multilayer neural networks set the current state of the art for many technical classification problems. But, these networks are still, essentially, black boxes in terms of analyzing them and predicting their performance. Here, we develop a statistical theory for the one-layer perceptron and show that it can predict performances of a surprisingly large variety of neural networks with different architectures. A general theory of classification with perceptrons is developed by generalizing an existing theory for analyzing reservoir computing models and connectionist models for symbolic reasoning known as vector symbolic architectures. Our statistical theory offers three formulas leveraging the signal statistics with increasing detail. The formulas are analytically intractable, but can be evaluated numerically. The description level that captures maximum details requires stochastic sampling methods. Depending on the network model, the simpler formulas already yield high prediction accuracy. The quality of the theory predictions is assessed in three experimental settings, a memorization task for echo state networks (ESNs) from reservoir computing literature, a collection of classification datasets for shallow randomly connected networks, and the ImageNet dataset for deep convolutional neural networks. We find that the second description level of the perceptron theory can predict the performance of types of ESNs, which could not be described previously. Furthermore, the theory can predict deep multilayer neural networks by being applied to their output layer. While other methods for prediction of neural networks performance commonly require to train an estimator model, the proposed theory requires only the first two moments of the distribution of the postsynaptic sums in the output neurons. Moreover, the perceptron theory compares favorably to other methods that do not rely on training an estimator model

Dynamic Rule Covering Classification in Data Mining with Cyber Security Phishing Application

Data mining is the process of discovering useful patterns from datasets using intelligent techniques to help users make certain decisions. A typical data mining task is classification, which involves predicting a target variable known as the class in previously unseen data based on models learnt from an input dataset. Covering is a well-known classification approach that derives models with If-Then rules. Covering methods, such as PRISM, have a competitive predictive performance to other classical classification techniques such as greedy, decision tree and associative classification. Therefore, Covering models are appropriate decision-making tools and users favour them carrying out decisions. Despite the use of Covering approach in data processing for different classification applications, it is also acknowledged that this approach suffers from the noticeable drawback of inducing massive numbers of rules making the resulting model large and unmanageable by users. This issue is attributed to the way Covering techniques induce the rules as they keep adding items to the rule’s body, despite the limited data coverage (number of training instances that the rule classifies), until the rule becomes with zero error. This excessive learning overfits the training dataset and also limits the applicability of Covering models in decision making, because managers normally prefer a summarised set of knowledge that they are able to control and comprehend rather a high maintenance models. In practice, there should be a trade-off between the number of rules offered by a classification model and its predictive performance. Another issue associated with the Covering models is the overlapping of training data among the rules, which happens when a rule’s classified data are discarded during the rule discovery phase. Unfortunately, the impact of a rule’s removed data on other potential rules is not considered by this approach. However, When removing training data linked with a rule, both frequency and rank of other rules’ items which have appeared in the removed data are updated. The impacted rules should maintain their true rank and frequency in a dynamic manner during the rule discovery phase rather just keeping the initial computed frequency from the original input dataset. In response to the aforementioned issues, a new dynamic learning technique based on Covering and rule induction, that we call Enhanced Dynamic Rule Induction (eDRI), is developed. eDRI has been implemented in Java and it has been embedded in WEKA machine learning tool. The developed algorithm incrementally discovers the rules using primarily frequency and rule strength thresholds. These thresholds in practice limit the search space for both items as well as potential rules by discarding any with insufficient data representation as early as possible resulting in an efficient training phase. More importantly, eDRI substantially cuts down the number of training examples scans by continuously updating potential rules’ frequency and strength parameters in a dynamic manner whenever a rule gets inserted into the classifier. In particular, and for each derived rule, eDRI adjusts on the fly the remaining potential rules’ items frequencies as well as ranks specifically for those that appeared within the deleted training instances of the derived rule. This gives a more realistic model with minimal rules redundancy, and makes the process of rule induction efficient and dynamic and not static. Moreover, the proposed technique minimises the classifier’s number of rules at preliminary stages by stopping learning when any rule does not meet the rule’s strength threshold therefore minimising overfitting and ensuring a manageable classifier. Lastly, eDRI prediction procedure not only priorities using the best ranked rule for class forecasting of test data but also restricts the use of the default class rule thus reduces the number of misclassifications. The aforementioned improvements guarantee classification models with smaller size that do not overfit the training dataset, while maintaining their predictive performance. The eDRI derived models particularly benefit greatly users taking key business decisions since they can provide a rich knowledge base to support their decision making. This is because these models’ predictive accuracies are high, easy to understand, and controllable as well as robust, i.e. flexible to be amended without drastic change. eDRI applicability has been evaluated on the hard problem of phishing detection. Phishing normally involves creating a fake well-designed website that has identical similarity to an existing business trustful website aiming to trick users and illegally obtain their credentials such as login information in order to access their financial assets. The experimental results against large phishing datasets revealed that eDRI is highly useful as an anti-phishing tool since it derived manageable size models when compared with other traditional techniques without hindering the classification performance. Further evaluation results using other several classification datasets from different domains obtained from University of California Data Repository have corroborated eDRI’s competitive performance with respect to accuracy, number of knowledge representation, training time and items space reduction. This makes the proposed technique not only efficient in inducing rules but also effective