3,950 research outputs found
Network Model Selection for Task-Focused Attributed Network Inference
Networks are models representing relationships between entities. Often these
relationships are explicitly given, or we must learn a representation which
generalizes and predicts observed behavior in underlying individual data (e.g.
attributes or labels). Whether given or inferred, choosing the best
representation affects subsequent tasks and questions on the network. This work
focuses on model selection to evaluate network representations from data,
focusing on fundamental predictive tasks on networks. We present a modular
methodology using general, interpretable network models, task neighborhood
functions found across domains, and several criteria for robust model
selection. We demonstrate our methodology on three online user activity
datasets and show that network model selection for the appropriate network task
vs. an alternate task increases performance by an order of magnitude in our
experiments
Learning to Rank based on Analogical Reasoning
Object ranking or "learning to rank" is an important problem in the realm of
preference learning. On the basis of training data in the form of a set of
rankings of objects represented as feature vectors, the goal is to learn a
ranking function that predicts a linear order of any new set of objects. In
this paper, we propose a new approach to object ranking based on principles of
analogical reasoning. More specifically, our inference pattern is formalized in
terms of so-called analogical proportions and can be summarized as follows:
Given objects , if object is known to be preferred to , and
relates to as relates to , then is (supposedly) preferred to
. Our method applies this pattern as a main building block and combines it
with ideas and techniques from instance-based learning and rank aggregation.
Based on first experimental results for data sets from various domains (sports,
education, tourism, etc.), we conclude that our approach is highly competitive.
It appears to be specifically interesting in situations in which the objects
are coming from different subdomains, and which hence require a kind of
knowledge transfer.Comment: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 8
page
Influence of Personal Preferences on Link Dynamics in Social Networks
We study a unique network dataset including periodic surveys and electronic
logs of dyadic contacts via smartphones. The participants were a sample of
freshmen entering university in the Fall 2011. Their opinions on a variety of
political and social issues and lists of activities on campus were regularly
recorded at the beginning and end of each semester for the first three years of
study. We identify a behavioral network defined by call and text data, and a
cognitive network based on friendship nominations in ego-network surveys. Both
networks are limited to study participants. Since a wide range of attributes on
each node were collected in self-reports, we refer to these networks as
attribute-rich networks. We study whether student preferences for certain
attributes of friends can predict formation and dissolution of edges in both
networks. We introduce a method for computing student preferences for different
attributes which we use to predict link formation and dissolution. We then rank
these attributes according to their importance for making predictions. We find
that personal preferences, in particular political views, and preferences for
common activities help predict link formation and dissolution in both the
behavioral and cognitive networks.Comment: 12 page
(Psycho-)Analysis of Benchmark Experiments
It is common knowledge that certain characteristics of data sets -- such as linear separability or sample size -- determine the performance of learning algorithms. In this paper we propose a formal framework for investigations on this relationship.
The framework combines three, in their respective scientific discipline well-established, methods. Benchmark experiments are the method of choice in machine and statistical learning to compare algorithms with respect to a certain performance measure on particular data sets. To realize the interaction between data sets and algorithms, the data sets are characterized using statistical and information-theoretic measures; a common approach in the field of meta learning to decide which algorithms are suited to particular data sets. Finally, the performance ranking of algorithms on groups of data sets with similar characteristics is determined by means of recursively partitioning Bradley-Terry models, that are commonly used in psychology to study the preferences of human subjects. The result is a tree with splits in data set characteristics which significantly change the performances of the algorithms. The main advantage is the automatic detection of these important characteristics.
The framework is introduced using a simple artificial example. Its real-word usage is demonstrated by means of an application example consisting of thirteen well-known data sets and six common learning algorithms. All resources to replicate the examples are available online
What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?
Purpose:
The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.
Design/methodology/approach:
A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.
Findings:
The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.
Research limitations/implications:
The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.
Originality/value:
Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective
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