An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage

The performance of Global Positioning System and Inertial Navigation System (GPS/INS) integrated navigation is reduced when GPS is blocked. This paper proposes an algorithm to overcome the condition where GPS is unavailable. Together with a parameter-optimised Genetic Algorithm (GA), a Support Vector Regression (SVR) algorithm is used to construct the mapping function between the specific force, angular rate increments of INS measurements and the increments of the GPS position. During GPS outages, the real-time pseudo-GPS position is predicted with the mapping function, and the corresponding covariance matrix is estimated by an improved adaptive filtering algorithm. A GPS/INS integration scheme is demonstrated where the vehicle travels along a straight line and around a curve, with respect to both low-speed-stable and high-speed-unstable navigation platforms. The results show that the proposed algorithm provides a better performance when GPS is unavailable

Solving Support Vector Machines in Reproducing Kernel Banach Spaces with Positive Definite Functions

In this paper we solve support vector machines in reproducing kernel Banach spaces with reproducing kernels defined on nonsymmetric domains instead of the traditional methods in reproducing kernel Hilbert spaces. Using the orthogonality of semi-inner-products, we can obtain the explicit representations of the dual (normalized-duality-mapping) elements of support vector machine solutions. In addition, we can introduce the reproduction property in a generalized native space by Fourier transform techniques such that it becomes a reproducing kernel Banach space, which can be even embedded into Sobolev spaces, and its reproducing kernel is set up by the related positive definite function. The representations of the optimal solutions of support vector machines (regularized empirical risks) in these reproducing kernel Banach spaces are formulated explicitly in terms of positive definite functions, and their finite numbers of coefficients can be computed by fixed point iteration. We also give some typical examples of reproducing kernel Banach spaces induced by Mat\'ern functions (Sobolev splines) so that their support vector machine solutions are well computable as the classical algorithms. Moreover, each of their reproducing bases includes information from multiple training data points. The concept of reproducing kernel Banach spaces offers us a new numerical tool for solving support vector machines.Comment: 26 page

Digging into acceptor splice site prediction : an iterative feature selection approach

Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction. We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature. The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets

THE EFFECTIVENESS OF POINT-OF-VIEW-VIDEO MODELING ON IMPROVING SOCIAL AND COMMUNICATION SKILLS

Autism spectrum disorder (ASD) is a complex neurobiological disorder with symptoms that affect individuals’ social interaction capabilities, their verbal and nonverbal communications, and the repertoires of activities and interest. These deficits in social and communication skills directly or indirectly influence the individual with ASDs’ lives. Therefore, many interventions have been developed to increase social and communication skills for individuals with ASD. Video modeling (VM) is one of the effective interventions in teaching social communication skills for children with ASD. There are multiple variations of VM interventions. One of the forms of VM intervention is point-of-view video modeling (POVVM) that has been potential to address the deficits of social communication skills and improve these skills for children with ASD. In this intervention, videos are filmed from the perspectives of the person who is the target of the intervention. The camera angle is presented with activity, skill, or context. The POVVM directly presents relevant stimuli and eliminates irrelevant stimuli of the target behavior. Thus, the POVVM intervention has provided a clear frame of imitating the behavior. The current study investigated the effectiveness of point-of-view video modeling on improving social initiation skills for young children with ASD. Three preschool-age children with ASD were implemented multiple-baseline across participants design to improve social initiation skills using point-of-view video modeling intervention. Specifically, the participants were taught greetings and engaging play activity behaviors using POVVM intervention. All participants improved their greetings and engaging play activity behaviors. The result of the study showed that POVVM is an effective intervention for improving social initiation skills. Consideration for interpretation and recommendations for future research are discussed

Determining the Level of Understanding and Misconceptions of Science Teacher Candidates about the Concepts Related to Material and Its Properties

This study was carried out to determine the level of understanding and misconceptions of science teacher candidates about some concepts related to material and its properties. This research was carried out with 260 science teacher candidates in Science Teaching Education Program of Education Faculty of Necmettin Erbakan University in 2015-2016 academic year. There are 17 scientifically and logically true and false sentences in the questionnaire in order to determine the level of understanding of science teacher candidates about some concepts related to material and its properties. In addition, in order to measure the participation degree of candidate teachers, three options were offered; “I agree”, “I do not agree” and “I have no idea”. The data obtained from this 3-point Likert scale questionnaire were analyzed statistically. When the obtained data were evaluated, it was determined that teacher candidates had misconceptions about some concepts related to material and its properties, and some suggestions were made to eliminate these misconceptions. Keywords: Chemistry education, Misconception, Material and its properties, Level of understandin

Identify error-sensitive patterns by decision tree

© Springer International Publishing Switzerland 2015. When errors are inevitable during data classification, finding a particular part of the classification model which may be more susceptible to error than others, when compared to finding an Achilles’ heel of the model in a casual way, may help uncover specific error-sensitive value patterns and lead to additional error reduction measures. As an initial phase of the investigation, this study narrows the scope of problem by focusing on decision trees as a pilot model, develops a simple and effective tagging method to digitize individual nodes of a binary decision tree for node-level analysis, to link and track classification statistics for each node in a transparent way, to facilitate the identification and examination of the potentially “weakest” nodes and error-sensitive value patterns in decision trees, to assist cause analysis and enhancement development. This digitization method is not an attempt to re-develop or transform the existing decision tree model, but rather, a pragmatic node ID formulation that crafts numeric values to reflect the tree structure and decision making paths, to expand post-classification analysis to detailed node-level. Initial experiments have shown successful results in locating potentially high-risk attribute and value patterns; this is an encouraging sign to believe this study worth further exploration

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented