Ensembles of wrappers for automated feature selection in fish age classification

In feature selection, the most important features must be chosen so as to decrease the number thereof while retaining their discriminatory information. Within this context, a novel feature selection method based on an ensemble of wrappers is proposed and applied for automatically select features in fish age classification. The effectiveness of this procedure using an Atlantic cod database has been tested for different powerful statistical learning classifiers. The subsets based on few features selected, e.g. otolith weight and fish weight, are particularly noticeable given current biological findings and practices in fishery research and the classification results obtained with them outperforms those of previous studies in which a manual feature selection was performed.Peer ReviewedPostprint (author's final draft

Reducing Spatial Data Complexity for Classification Models

Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which fiirther hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the comparable compression levels

AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images

We propose a new hybrid system for automatically generating and training quantum-inspired classifiers on grayscale images by using multiobjective genetic algorithms. We define a dynamic fitness function to obtain the smallest possible circuit and highest accuracy on unseen data, ensuring that the proposed technique is generalizable and robust. We minimize the complexity of the generated circuits in terms of the number of entanglement gates by penalizing their appearance. We reduce the size of the images with two dimensionality reduction approaches: principal component analysis (PCA), which is encoded in the individual for optimization purpose, and a small convolutional autoencoder (CAE). These two methods are compared with one another and with a classical nonlinear approach to understand their behaviors and to ensure that the classification ability is due to the quantum circuit and not the preprocessing technique used for dimensionality reduction.Comment: Submitted for review on the 7th of June 202

CCNETS: A Novel Brain-Inspired Approach for Enhanced Pattern Recognition in Imbalanced Datasets

This study introduces CCNETS (Causal Learning with Causal Cooperative Nets), a novel generative model-based classifier designed to tackle the challenge of generating data for imbalanced datasets in pattern recognition. CCNETS is uniquely crafted to emulate brain-like information processing and comprises three main components: Explainer, Producer, and Reasoner. Each component is designed to mimic specific brain functions, which aids in generating high-quality datasets and enhancing classification performance. The model is particularly focused on addressing the common and significant challenge of handling imbalanced datasets in machine learning. CCNETS's effectiveness is demonstrated through its application to a "fraud dataset," where normal transactions significantly outnumber fraudulent ones (99.83% vs. 0.17%). Traditional methods often struggle with such imbalances, leading to skewed performance metrics. However, CCNETS exhibits superior classification ability, as evidenced by its performance metrics. Specifically, it achieved an F1-score of 0.7992, outperforming traditional models like Autoencoders and Multi-layer Perceptrons (MLP) in the same context. This performance indicates CCNETS's proficiency in more accurately distinguishing between normal and fraudulent patterns. The innovative structure of CCNETS enhances the coherence between generative and classification models, helping to overcome the limitations of pattern recognition that rely solely on generative models. This study emphasizes CCNETS's potential in diverse applications, especially where quality data generation and pattern recognition are key. It proves effective in machine learning, particularly for imbalanced datasets. CCNETS overcomes current challenges in these datasets and advances machine learning with brain-inspired approaches.Comment: 31 pages, authors (3) is Corresponding Autho

Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification

Recent years have seen significant progress in improving both the efficiency and effectiveness of time series classification. However, because the best solution is typically the Nearest Neighbor algorithm with the relatively expensive Dynamic Time Warping as the distance measure, successful deployments on resource constrained devices remain elusive. Moreover, the recent explosion of interest in wearable devices, which typically have limited computational resources, has created a growing need for very efficient classification algorithms. A commonly used technique to glean the benefits of the Nearest Neighbor algorithm, without inheriting its undesirable time complexity, is to use the Nearest Centroid algorithm. However, because of the unique properties of (most) time series data, the centroid typically does not resemble any of the instances, an unintuitive and underappreciated fact. In this work we show that we can exploit a recent result to allow meaningful averaging of 'warped' times series, and that this result allows us to create ultra-efficient Nearest 'Centroid' classifiers that are at least as accurate as their more lethargic Nearest Neighbor cousins

Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm

A concerted research effort over the past two decades has heralded significant improvements in both the efficiency and effectiveness of time series classification. The consensus that has emerged in the community is that the best solution is a surprisingly simple one. In virtually all domains, the most accurate classifier is the nearest neighbor algorithm with dynamic time warping as the distance measure. The time complexity of dynamic time warping means that successful deployments on resource-constrained devices remain elusive. Moreover, the recent explosion of interest in wearable computing devices, which typically have limited computational resources, has greatly increased the need for very efficient classification algorithms. A classic technique to obtain the benefits of the nearest neighbor algorithm, without inheriting its undesirable time and space complexity, is to use the nearest centroid algorithm. Unfortunately, the unique properties of (most) time series data mean that the centroid typically does not resemble any of the instances, an unintuitive and underappreciated fact. In this paper we demonstrate that we can exploit a recent result by Petitjean et al. to allow meaningful averaging of “warped” time series, which then allows us to create super-efficient nearest “centroid” classifiers that are at least as accurate as their more computationally challenged nearest neighbor relatives. We demonstrate empirically the utility of our approach by comparing it to all the appropriate strawmen algorithms on the ubiquitous UCR Benchmarks and with a case study in supporting insect classification on resource-constrained sensors

Development and Investigation of Cost-Sensitive Pruned Decision Tree Model for Improved Schizophrenia Diagnosis

Schizophrenia is often characterized by delusions, hallucinations, and other cognitive difficulties, affects approximately seventy million adults globally. This study presents a cost-sensitive pruned Decision Tree J48 model for fast and accurate diagnosis of Schizophrenia. The model implements supervised learning procedures with 10-fold cross-validation resampling method and utilizes unstructured filter to replace missing values in the data with the modal values of corresponding features. Features are selected using Pearson’s correlation on hot-coded data to detect redundancy in data. Cost matrix is designed to minimize the tendencies of the J48 algorithm to predict false negative outcomes. This consequently reduces the error of the model in diagnosing a Schizophrenia candidate as free from the disease. The model is found to significantly diagnose Schizophrenia with 78% accuracy, 89.7% sensitivity, 57.4% specificity and Area under the Receiver Operator Characteristic (ROC) curve of 0.895. The ROC curve is also seen to distinguish Schizophrenia from other conditions with similar symptoms. These results show the potential of machine-learning models for quick, effective diagnosis of schizophrenia

Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is known that the performance of supervised learning approaches highly relies on the availability and quality of labeled training data. In Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient fault data has posed a major challenge to applying these supervised learning techniques to commercial buildings. To overcome this challenge, this paper proposes using Monte Carlo dropout (MC-dropout) to enhance the supervised learning pipeline, so that the resulting neural network is able to detect and diagnose unseen incipient fault examples. We also examine the proposed MC-dropout method on the RP-1043 dataset to demonstrate its effectiveness in indicating the most likely incipient fault types

A Predictive maintenance model for heterogeneous industrial refrigeration systems

The automatic assessment of the degradation state of industrial refrigeration systems is becoming increasingly important and constitutes a key-role within predictive maintenance approaches. Lately, data-driven methods especially became the focus of research in this respect. As they only rely on historical data in the development phase, they offer great advantages in terms of flexibility and generalisability by circumventing the need for specific domain knowledge. While most scientific contributions employ methods emerging from the field of machine learning (ML), only very few consider their applicability amongst different heterogeneous systems. In fact, the majority of existing contributions in this field solely apply supervised ML models, which assume the availability of labelled fault data for each system respectively. However, this places restrictions on the overall applicability, as data labelling is mostly conducted by humans and therefore constitutes a non-negligible cost and time factor. Moreover, such methods assume that all considered fault types occurred in the past, a condition that may not always be guaranteed to be satisfied. Therefore, this dissertation proposes a predictive maintenance model for industrial refrigeration systems by especially addressing its transferability onto different but related heterogeneous systems. In particular, it aims at solving a sub-problem known as condition-based maintenance (CBM) to automatically assess the system’s state of degradation. To this end, the model does not only estimate how far a possible malfunction has progressed, but also determines the fault type being present. As will be described in greater detail throughout this dissertation, the proposed model also utilises techniques from the field of ML but rather bypasses the strict assumptions accompanying supervised ML. Accordingly, it assumes the data of the target system to be primarily unlabelled while a few labelled samples are expected to be retrievable from the fault-free operational state, which can be obtained at low cost. Yet, to enable the model’s intended functionality, it additionally employs data from only one fully labelled source dataset and, thus, allows the benefits of data-driven approaches towards predictive maintenance to be further exploited. After the introduction, the dissertation at hand introduces the related concepts as well as the terms and definitions and delimits this work from other fields of research. Furthermore, the scope of application is further introduced and the latest scientific work is presented. This is then followed by the explanation of the open research gap, from which the research questions are derived. The third chapter deals with the main principles of the model, including the mathematical notations and the individual concepts. It furthermore delivers an overview about the variety of problems arising in this context and presents the associated solutions from a theoretical point of view. Subsequently, the data acquisition phase is described, addressing both the data collection procedure and the outcome of the test cases. In addition, the considered fault characteristics are presented and compared with the ones obtained from the related publicly available dataset. In essence, both datasets form the basis for the model validation, as discussed in the following chapter. This chapter then further comprises the results obtained from the model, which are compared with the ones retrieved from several baseline models derived from the literature. This work then closes with a summary and the conclusions drawn from the model results. Lastly, an outlook of the presented dissertation is provide