Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data

There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Third, emotional data often suffers from missing modalities due to unforeseeable sensor malfunction or configuration issues. In this paper, we address all these problems under a novel multi-view deep generative framework. Specifically, we propose to model the statistical relationships of multi-modality emotional data using multiple modality-specific generative networks with a shared latent space. By imposing a Gaussian mixture assumption on the posterior approximation of the shared latent variables, our framework can learn the joint deep representation from multiple modalities and evaluate the importance of each modality simultaneously. To solve the labeled-data-scarcity problem, we extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. To address the missing-modality problem, we further extend our semi-supervised multi-view model to deal with incomplete data, where a missing view is treated as a latent variable and integrated out during inference. This way, the proposed overall framework can utilize all available (both labeled and unlabeled, as well as both complete and incomplete) data to improve its generalization ability. The experiments conducted on two real multi-modal emotion datasets demonstrated the superiority of our framework.Comment: arXiv admin note: text overlap with arXiv:1704.07548, 2018 ACM Multimedia Conference (MM'18

Effects of Data Imputation Methods on Data Missingness in Data Mining

The purpose of this paper is to study theeffectiveness of data imputation methods in dealingwith data missingness in the data mining phase ofknowledge discovery in Database (KDD). Theapplication of data mining techniques without carefulconsideration of missing data can result into biasedresults and skewed conclusions. This research exploresthe impact of data missingness at various levels in KDDmodels employing neural networks as the primary datamining algorithm. Four of the most commonly utilizeddata imputation methods - Case Deletion, MeanSubstitution, Regression Imputation, and MultipleImputation were evalutated using Root Mean Square(RMS) Values, ANOVA Testing, T-tests, and Tukey’sHonestly Significant Difference Test to assess thedifferences of performance levels between variousKnowledge Discovery and Neural Network Models,both in the presence and absence of Missing Data

The Impact of Data Imputation Methodologies on Knowledge Discovery

The purpose of this research is to investigate the impact of Data Imputation Methodologies that are employed when a specific Data Mining algorithm is utilized within a KDD (Knowledge Discovery in Databases) process. This study will employ certain Knowledge Discovery processes that are widely accepted in both the academic and commercial worlds. Several Knowledge Discovery models will be developed utilizing secondary data containing known correct values. Tests will be conducted on the secondary data both before and after storing data instances with known results and then identifying imprecise data values. One of the integral stages in the accomplishment of successful Knowledge Discovery is the Data Mining phase. The actual Data Mining process deals significantly with prediction, estimation, classification, pattern recognition and the development of association rules. Neural Networks are the most commonly selected tools for Data Mining classification and prediction. Neural Networks employ various types of Transfer Functions when outputting data. The most commonly employed Transfer Function is the s-Sigmoid Function. Various Knowledge Discovery Models from various research and business disciplines were tested using this framework. However, missing and inconsistent data has been pervasive problems in the history of data analysis since the origin of data collection. Due to advancements in the capacities of data storage and the proliferation of computer software, more historical data is being collected and analyzed today than ever before. The issue of missing data must be addressed, since ignoring this problem can introduce bias into the models being evaluated and lead to inaccurate data mining conclusions. The objective of this research is to address the impact of Missing Data and Data Imputation on the Data Mining phase of Knowledge Discovery when Neural Networks are utilized when employing an s-Sigmoid Transfer function, and are confronted with Missing Data and Data Imputation methodologie

The Impact of Data Imputation Methodologies on Knowledge Discovery

The purpose of this research is to investigate the impact of Data Imputation Methodologies that are employed when a specific Data Mining algorithm is utilized within a KDD (Knowledge Discovery in Databases) process. This study will employ certain Knowledge Discovery processes that are widely accepted in both the academic and commercial worlds. Several Knowledge Discovery models will be developed utilizing secondary data containing known correct values. Tests will be conducted on the secondary data both before and after storing data instances with known results and then identifying imprecise data values. One of the integral stages in the accomplishment of successful Knowledge Discovery is the Data Mining phase. The actual Data Mining process deals significantly with prediction, estimation, classification, pattern recognition and the development of association rules. Neural Networks are the most commonly selected tools for Data Mining classification and prediction. Neural Networks employ various types of Transfer Functions when outputting data. The most commonly employed Transfer Function is the s-Sigmoid Function. Various Knowledge Discovery Models from various research and business disciplines were tested using this framework. However, missing and inconsistent data has been pervasive problems in the history of data analysis since the origin of data collection. Due to advancements in the capacities of data storage and the proliferation of computer software, more historical data is being collected and analyzed today than ever before. The issue of missing data must be addressed, since ignoring this problem can introduce bias into the models being evaluated and lead to inaccurate data mining conclusions. The objective of this research is to address the impact of Missing Data and Data Imputation on the Data Mining phase of Knowledge Discovery when Neural Networks are utilized when employing an s-Sigmoid Transfer function, and are confronted with Missing Data and Data Imputation methodologie

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