Hadoop neural network for parallel and distributed feature selection

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop

Adam Deep Learning with SOM for Human Sentiment Classification

Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing

A Review of Infrastructures to Process Big Multimedia Data

In the last years, the volume of information is growing faster than ever before, moving from small to huge, structured to unstructured datasets like text, image, audio and video. The purpose of processing the data is aimed to extract relevant information on trends, challenges and opportunities; all these studies with large volumes of data. The increase in the power of parallel computing enabled the use of Machine Learning (ML) techniques to take advantage of the processing capabilities offered by new architectures on large volumes of data. For this reason, it is necessary to find mechanisms that allow classify and organize them to facilitate to the users the extraction of the required information. The processing of these data requires the use of classification techniques that will be reviewed. This work analyzes different studies carried out on the use of ML for processing large volumes of data (Big Multimedia Data) and proposes a classification, using as criteria, the hardware infrastructures used in works of machine learning parallel approaches applied to large volumes of data

AI and Automatic Music Generation for Mindfulness

This paper presents an architecture for the creation of emotionally congruent music using machine learning aided sound synthesis. Our system can generate a small corpus of music using Hidden Markov Models; we can label the pieces with emotional tags using data elicited from questionnaires. This produces a corpus of labelled music underpinned by perceptual evaluations. We then analyse participant’s galvanic skin response (GSR) while listening to our generated music pieces and the emotions they describe in a questionnaire conducted after listening. These analyses reveal that there is a direct correlation between the calmness/scariness of a musical piece, the users’ GSR reading and the emotions they describe feeling. From these, we will be able to estimate an emotional state using biofeedback as a control signal for a machine-learning algorithm, which generates new musical structures according to a perceptually informed musical feature similarity model. Our case study suggests various applications including in gaming, automated soundtrack generation, and mindfulness

Knowledge management overview of feature selection problem in high-dimensional financial data: Cooperative co-evolution and Map Reduce perspectives

The term big data characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs volume, velocity, variety, and veracity-to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-Time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-Time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-And-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-To-use distributed, scalable, and fault-Tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-The-Art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions