Feature selection for high dimensional imbalanced class data using harmony search

Misclassification costs of minority class data in real-world applications can be very high. This is a challenging problem especially when the data is also high in dimensionality because of the increase in overfitting and lower model interpretability. Feature selection is recently a popular way to address this problem by identifying features that best predict a minority class. This paper introduces a novel feature selection method call SYMON which uses symmetrical uncertainty and harmony search. Unlike existing methods, SYMON uses symmetrical uncertainty to weigh features with respect to their dependency to class labels. This helps to identify powerful features in retrieving the least frequent class labels. SYMON also uses harmony search to formulate the feature selection phase as an optimisation problem to select the best possible combination of features. The proposed algorithm is able to deal with situations where a set of features have the same weight, by incorporating two vector tuning operations embedded in the harmony search process. In this paper, SYMON is compared against various benchmark feature selection algorithms that were developed to address the same issue. Our empirical evaluation on different micro-array data sets using G-Mean and AUC measures confirm that SYMON is a comparable or a better solution to current benchmarks

Intrusion Detection: Embedded Software Machine Learning and Hardware Rules Based Co-Designs

Security of innovative technologies in future generation networks such as (Cyber Physical Systems (CPS) and Wi-Fi has become a critical universal issue for individuals, economy, enterprises, organizations and governments. The rate of cyber-attacks has increased dramatically, and the tactics used by the attackers are continuing to evolve and have become ingenious during the attacks. Intrusion Detection is one of the solutions against these attacks. One approach in designing an intrusion detection system (IDS) is software-based machine learning. Such approach can predict and detect threats before they result in major security incidents. Moreover, despite the considerable research in machine learning based designs, there is still a relatively small body of literature that is concerned with imbalanced class distributions from the intrusion detection system perspective. In addition, it is necessary to have an effective performance metric that can compare multiple multi-class as well as binary-class systems with respect to class distribution. Furthermore, the expectant detection techniques must have the ability to identify real attacks from random defects, ingrained defects in the design, misconfigurations of the system devices, system faults, human errors, and software implementation errors. Moreover, a lightweight IDS that is small, real-time, flexible and reconfigurable enough to be used as permanent elements of the system's security infrastructure is essential. The main goal of the current study is to design an effective and accurate intrusion detection framework with minimum features that are more discriminative and representative. Three publicly available datasets representing variant networking environments are adopted which also reflect realistic imbalanced class distributions as well as updated attack patterns. The presented intrusion detection framework is composed of three main modules: feature selection and dimensionality reduction, handling imbalanced class distributions, and classification. The feature selection mechanism utilizes searching algorithms and correlation based subset evaluation techniques, whereas the feature dimensionality reduction part utilizes principal component analysis and auto-encoder as an instance of deep learning. Various classifiers, including eight single-learning classifiers, four ensemble classifiers, one stacked classifier, and five imbalanced class handling approaches are evaluated to identify the most efficient and accurate one(s) for the proposed intrusion detection framework. A hardware-based approach to detect malicious behaviors of sensors and actuators embedded in medical devices, in which the safety of the patient is critical and of utmost importance, is additionally proposed. The idea is based on a methodology that transforms a device's behavior rules into a state machine to build a Behavior Specification Rules Monitoring (BSRM) tool for four medical devices. Simulation and synthesis results demonstrate that the BSRM tool can effectively identify the expected normal behavior of the device and detect any deviation from its normal behavior. The performance of the BSRM approach has also been compared with a machine learning based approach for the same problem. The FPGA module of the BSRM can be embedded in medical devices as an IDS and can be further integrated with the machine learning based approach. The reconfigurable nature of the FPGA chip adds an extra advantage to the designed model in which the behavior rules can be easily updated and tailored according to the requirements of the device, patient, treatment algorithm, and/or pervasive healthcare application

A systematic review of data quality issues in knowledge discovery tasks

Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

Filter � GA Based Approach to Feature Selection for Classification

This paper presents a new approach to select reduced number of features in databases. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as and can confuse the process of classification. The proposed method applies filter attribute measure and binary coded Genetic Algorithm to select a small subset of features. The importance of these features is judged by applying K-nearest neighbor (KNN) method of classification. The best reduced subset of features which has high classification accuracy on given databases is adopted. The classification accuracy obtained by proposed method is compared with that reported recently in publications on twenty eight databases. It is noted that proposed method performs satisfactory on these databases and achieves higher classification accuracy but with smaller number of features

Computational intelligent methods for trusting in social networks

104 p.This Thesis covers three research lines of Social Networks. The first proposed reseach line is related with Trust. Different ways of feature extraction are proposed for Trust Prediction comparing results with classic methods. The problem of bad balanced datasets is covered in this work. The second proposed reseach line is related with Recommendation Systems. Two experiments are proposed in this work. The first experiment is about recipe generation with a bread machine. The second experiment is about product generation based on rating given by users. The third research line is related with Influence Maximization. In this work a new heuristic method is proposed to give the minimal set of nodes that maximizes the influence of the network

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection

Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced by banks. There following are the critical challenges involved in fraud datasets: the dataset is highly imbalanced, the fraud pattern is changing, etc. Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One class classification (OCC). In this study, we handled these techniques on an ATM transactions dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Further, we employed various machine learning techniques viz., Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958 AUC. DT is the winner if the complexity and interpretability aspects are considered. Among all the oversampling approaches, SMOTE and its variants were observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM secured second place with 0.947 CR. Further, we incorporated explainable artificial intelligence (XAI) and causal inference (CI) in the fraud detection framework and studied it through various analyses.Comment: 34 pages; 21 Figures; 8 Table

Improved probabilistic distance based locality preserving projections method to reduce dimensionality in large datasets

In this paper, a dimensionality reduction is achieved in large datasets using the proposed distance based Non-integer Matrix Factorization (NMF) technique, which is intended to solve the data dimensionality problem. Here, NMF and distance measurement aim to resolve the non-orthogonality problem due to increased dataset dimensionality. It initially partitions the datasets, organizes them into a defined geometric structure and it avoids capturing the dataset structure through a distance based similarity measurement. The proposed method is designed to fit the dynamic datasets and it includes the intrinsic structure using data geometry. Therefore, the complexity of data is further avoided using an Improved Distance based Locality Preserving Projection. The proposed method is evaluated against existing methods in terms of accuracy, average accuracy, mutual information and average mutual information