Competition and Success in the Meme Pool: a Case Study on Quickmeme.com

The advent of social media has provided data and insights about how people relate to information and culture. While information is composed by bits and its fundamental building bricks are relatively well understood, the same cannot be said for culture. The fundamental cultural unit has been defined as a "meme". Memes are defined in literature as specific fundamental cultural traits, that are floating in their environment together. Just like genes carried by bodies, memes are carried by cultural manifestations like songs, buildings or pictures. Memes are studied in their competition for being successfully passed from one generation of minds to another, in different ways. In this paper we choose an empirical approach to the study of memes. We downloaded data about memes from a well-known website hosting hundreds of different memes and thousands of their implementations. From this data, we empirically describe the behavior of these memes. We statistically describe meme occurrences in our dataset and we delineate their fundamental traits, along with those traits that make them more or less apt to be successful

Credit Scoring Using Machine Learning

For financial institutions and the economy at large, the role of credit scoring in lending decisions cannot be overemphasised. An accurate and well-performing credit scorecard allows lenders to control their risk exposure through the selective allocation of credit based on the statistical analysis of historical customer data. This thesis identifies and investigates a number of specific challenges that occur during the development of credit scorecards. Four main contributions are made in this thesis. First, we examine the performance of a number supervised classification techniques on a collection of imbalanced credit scoring datasets. Class imbalance occurs when there are significantly fewer examples in one or more classes in a dataset compared to the remaining classes. We demonstrate that oversampling the minority class leads to no overall improvement to the best performing classifiers. We find that, in contrast, adjusting the threshold on classifier output yields, in many cases, an improvement in classification performance. Our second contribution investigates a particularly severe form of class imbalance, which, in credit scoring, is referred to as the low-default portfolio problem. To address this issue, we compare the performance of a number of semi-supervised classification algorithms with that of logistic regression. Based on the detailed comparison of classifier performance, we conclude that both approaches merit consideration when dealing with low-default portfolios. Third, we quantify the differences in classifier performance arising from various implementations of a real-world behavioural scoring dataset. Due to commercial sensitivities surrounding the use of behavioural scoring data, very few empirical studies which directly address this topic are published. This thesis describes the quantitative comparison of a range of dataset parameters impacting classification performance, including: (i) varying durations of historical customer behaviour for model training; (ii) different lengths of time from which a borrower’s class label is defined; and (iii) using alternative approaches to define a customer’s default status in behavioural scoring. Finally, this thesis demonstrates how artificial data may be used to overcome the difficulties associated with obtaining and using real-world data. The limitations of artificial data, in terms of its usefulness in evaluating classification performance, are also highlighted. In this work, we are interested in generating artificial data, for credit scoring, in the absence of any available real-world data

R-miss-tastic: a unified platform for missing values methods and workflows

Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and visualization methods require complete data, and improper handling of missing data results in information loss, or biased analyses. Since the seminal work of Rubin (1976), there has been a burgeoning literature on missing values with heterogeneous aims and motivations. This has resulted in the development of various methods, formalizations, and tools (including a large number of R packages and Python modules). However, for practitioners, it remains challenging to decide which method is most suited for their problem, partially because handling missing data is still not a topic systematically covered in statistics or data science curricula. To help address this challenge, we have launched a unified platform: "R-miss-tastic", which aims to provide an overview of standard missing values problems, methods, how to handle them in analyses, and relevant implementations of methodologies. In the same perspective, we have also developed several pipelines in R and Python to allow for a hands-on illustration of how to handle missing values in various statistical tasks such as estimation and prediction, while ensuring reproducibility of the analyses. This will hopefully also provide some guidance on deciding which method to choose for a specific problem and data. The objective of this work is not only to comprehensively organize materials, but also to create standardized analysis workflows, and to provide a common ground for discussions among the community. This platform is thus suited for beginners, students, more advanced analysts and researchers.Comment: 38 pages, 9 figure

Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.Comment: ACM SIGMOD Conference 2016, 18 pages, 29 figure

A Survey on Concept Drift Adaptation

Concept drift primarily refers to an online supervised learning scenario when the relation between the in- put data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for re- searchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art

Forecasting with Machine Learning

For years, people have been forecasting weather patterns, economic and political events, sports outcomes, and more. In this paper we discussed the ways of using machine learning in forecasting, machine learning is a branch of computer science where algorithms learn from data. The fundamental problem for machine learning and time series is the same: to predict new outcomes based on previously known results. Using the suitable technique of machine learning depend on how much data you have, how noisy the data is, and what kind of new features can be derived from the data. But these techniques can improve accuracy and don’t have to be difficult to implement