Self-adaptive attribute weighting for Naive Bayes classification

©2014 Elsevier Ltd. All rights reserved. Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance

Does mild COPD affect prognosis in the elderly?

<p>Abstract</p> <p>Background</p> <p>Chronic obstructive pulmonary disease (COPD) affects independence and survival in the general population, but it is unknown to which extent this conclusion applies to elderly people with mild disease. The aim of this study was to verify whether mild COPD, defined according to different classification systems (ATS/ERS, BTS, GOLD) impacts independence and survival in elderly (aged 65 to 74 years) or very elderly (aged 75 years or older) patients.</p> <p>Methods</p> <p>We used data coming from the Respiratory Health in the Elderly (Salute Respiratoria nell'Anziano, SaRA) study and compared the differences between the classification systems with regards to personal capabilities and 5-years survival, focusing on the mild stage of COPD.</p> <p>Results</p> <p>We analyzed data from 1,159 patients (49% women) with a mean age of 73.2 years (SD: 6.1). One third of participants were 75 years or older. Mild COPD, whichever was its definition, was not associated with worse personal capabilities or increased mortality after adjustment for potential confounders in both age groups.</p> <p>Conclusions</p> <p>Mild COPD may not affect survival or personal independence of patients over 65 years of age if the reference group consists of patients with a comparable burden of non respiratory diseases. Comorbidity and age itself likely are main determinants of both outcomes.</p

Study on Naive Bayesian Classifier and its relation to Information Gain

Classification and clustering techniques in d ata mining are useful for a wide variety of real time applications dealing with large amount o f data. Some of the application areas of data mining are text classification, medical diagnosis, intrusion detection systems etc . The Naive Bayes Classifier techn ique is based on the Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods. The approach is called "naïve" because it assumes the independence between the various attribute values. Naïve Bayes classification can be viewed as both a descriptive and a predictive type of algorithm. The probabilities are descriptive and are then used to predict the class membership for a untrained data

Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

The classification accuracy of one-dimensional (1D) biomedical signals is limited due to the lack of independence of the extracted features. To address this shortcoming, the study applies a swarm intelligence algorithm based on plant root systems (PRSs) to feature engineering. Some basic features of 1D biomedical signals are integrated into a digitized soil, and a root matrix is generated from this digitized soil and the PRS algorithm. The PRS features are extracted from the root matrix and used to classify the basic features. Following classification with the same biomedical signals and classifier, the accuracy of the added PRS set is generally higher than that of the base set. The result shows that the proposed algorithm can expand the application of 1D biomedical signals to include more biomedical signals in classification tasks for clinical diagnosis

The Classification and Perfomance of Alternative Exchange-Rate Systems

Owing to dissatisfaction with the IMF’s de jure classification of exchange-rate regimes, a substantial literature has emerged presenting de facto classifications of exchange-rate systems and using the latter classifications to compare performances of alternative regimes in terms of key macroeconomic variables. This paper critically reviews the literature on de facto regimes. In particular the paper (1) describes the main methodologies that have been used to construct de facto codings, (2) surveys the empirical literature generated by de facto regime codings, and (3) lays-out the problems inherent in constructing de facto classifications. The empirical literature is found to yield few robust findings. We argue that the as-yet unfulfilled objective of this literature, and the major research agenda for the future in this area, lies in the need of a more thorough investigation of the degree of monetary-policy independence without relying exclusively on movements in exchange rates, an agenda the attainment of which is made especially challenging because of the lack of comprehensive and reliable data on reserves and interest rates.Exchange-rate regimes; Economic growth; Inflation; Bipolar hypothesis

Lightweight human activity recognition for ambient assisted living

© 2023, IARIA.Ambient assisted living (AAL) systems aim to improve the safety, comfort, and quality of life for the populations with specific attention given to prolonging personal independence during later stages of life. Human activity recognition (HAR) plays a crucial role in enabling AAL systems to recognise and understand human actions. Multi-view human activity recognition (MV-HAR) techniques are particularly useful for AAL systems as they can use information from multiple sensors to capture different perspectives of human activities and can help to improve the robustness and accuracy of activity recognition. In this work, we propose a lightweight activity recognition pipeline that utilizes skeleton data from multiple perspectives to combine the advantages of both approaches and thereby enhance an assistive robot's perception of human activity. The pipeline includes data sampling, input data type, and representation and classification methods. Our method modifies a classic LeNet classification model (M-LeNet) and uses a Vision Transformer (ViT) for the classification task. Experimental evaluation on a multi-perspective dataset of human activities in the home (RH-HAR-SK) compares the performance of these two models and indicates that combining camera views can improve recognition accuracy. Furthermore, our pipeline provides a more efficient and scalable solution in the AAL context, where bandwidth and computing resources are often limited

A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities

The measurement and provision of precise and upto-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic controls systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data such as velocity of individual vehicles as well as vehicle type information can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%