20 research outputs found
On the Window Size for Classification in Changing Environments
Classification in changing environments (commonly known as concept drift) requires adaptation of the classifier to accommodate the
changes. One approach is to keep a moving window on the streaming data and constantly update the classifier on it. Here we consider an
abrupt change scenario where one set of probability distributions of the classes is instantly replaced with another. For a fixed ‘transition
period’ around the change, we derive a generic relationship between the size of the moving window and the classification error rate. We
derive expressions for the error in the transition period and for the optimal window size for the case of two Gaussian classes where the
concept change is a geometrical displacement of the whole class configuration in the space. A simple window resize strategy based
on the derived relationship is proposed and compared with fixed-size windows on a real benchmark data set data set (Electricity Market)
Adaptive algorithms for real-world transactional data mining.
The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a
key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related)
in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identification and classification of customers. Data models derived from the data mining process can aid in effectively recognizing the status and preference of customers - individually and as a group. Such
data models can be incorporated into the business market segmentation, customer targeting and channelling decisions with the goal of maximizing the total customer lifetime
profit. However, due to costs, privacy and/or data protection reasons, the customer data available for data mining is often restricted to verified and validated data,(in most cases,only the business owned transactional data is available). Transactional data is a valuable resource for generating such data models. Transactional data can be electronically collected and readily made available for data mining in large quantity at minimum extra
cost. Transactional data is however, inherently sparse and skewed. These inherent characteristics of transactional data give rise to the poor performance of data models built using customer data based on transactional data. Data models for identifying, describing, and classifying customers, constructed using evolving transactional data thus need to effectively handle the inherent sparseness and skewness of evolving transactional data in order
to be efficient and accurate. Using real-world transactional data, this thesis presents the
findings and results from the investigation of data mining algorithms for analysing, describing, identifying and classifying customers with evolving needs. In particular, methods for handling the issues of scalability, uncertainty and adaptation whilst mining evolving transactional data are analysed and presented. A novel application of a new framework for integrating transactional data binning and classification techniques is presented alongside
an effective prototype selection algorithm for efficient transactional data model building. A new change mining architecture for monitoring, detecting and visualizing the change in customer behaviour using transactional data is proposed and discussed as an effective means for analysing and understanding the change in customer buying behaviour
over time. Finally, the challenging problem of discerning between the change in the customer profile (which may necessitate the effective change of the customer’s label) and the change in performance of the model(s) (which may necessitate changing or adapting the model(s)) is introduced and discussed by way of a novel flexible and efficient architecture for classifier model adaptation and customer profiles class relabeling
Density Preserving Sampling: Robust and Efficient Alternative to Cross-validation for Error Estimation
Estimation of the generalization ability of a classi-
fication or regression model is an important issue, as it indicates
the expected performance on previously unseen data and is
also used for model selection. Currently used generalization
error estimation procedures, such as cross-validation (CV) or
bootstrap, are stochastic and, thus, require multiple repetitions
in order to produce reliable results, which can be computationally
expensive, if not prohibitive. The correntropy-inspired density-
preserving sampling (DPS) procedure proposed in this paper
eliminates the need for repeating the error estimation procedure
by dividing the available data into subsets that are guaranteed to
be representative of the input dataset. This allows the production
of low-variance error estimates with an accuracy comparable to
10 times repeated CV at a fraction of the computations required
by CV. This method can also be used for model ranking and
selection. This paper derives the DPS procedure and investigates
its usability and performance using a set of public benchmark
datasets and standard classifier
Izbor atributa integracijom znanja o domenu primenom metoda odlučivanja kod prediktivnog modelovanja vremenskih serija nadgledanim mašinskim učenjem
The aim of the research presented within this doctoral dissertation is
to develop a feature selection methodology through integrating
domain-specific knowledge by applying mathematical methods of
decision-making, to improve the feature selection process and the
precision of supervised machine learning methods for predictive
modeling of time series.
To integrate domain-specific knowledge, a multi-criteria decision
making method is used, i.e. an analytical hierarchical process proven
to be successful in numerous studies carried out to date. This
approach was selected because it allows the selection of a set of
factors based on their relevance, even in the case of mutually opposite
criteria.
In predicting the movement of time series, the possibility of
integrating feature relevance into support vector machines to improve
their prediction accuracy was studied.
The proposed methodology was applied as a feature-selection method
for the predictive modelling of movement of financial time series.
Unlike existing approaches, where the feature selection method is
based on a quantitative analysis of the input values, the proposed
methodology carries out a qualitative evaluation of the attributes in
relation to the prediction domain and represents a means of
integrating a priori knowledge of the prediction domain
Ubiquitous Technologies for Emotion Recognition
Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions
Representation Learning for Words and Entities
This thesis presents new methods for unsupervised learning of distributed
representations of words and entities from text and knowledge bases. The first
algorithm presented in the thesis is a multi-view algorithm for learning
representations of words called Multiview Latent Semantic Analysis (MVLSA). By
incorporating up to 46 different types of co-occurrence statistics for the same
vocabulary of english words, I show that MVLSA outperforms other
state-of-the-art word embedding models. Next, I focus on learning entity
representations for search and recommendation and present the second method of
this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an
unsupervised learning method, but it is based on the Variational Autoencoder
framework. Evaluations with human annotators show that NVSE can facilitate
better search and recommendation of information gathered from noisy, automatic
annotation of unstructured natural language corpora. Finally, I move from
unstructured data and focus on structured knowledge graphs. I present novel
approaches for learning embeddings of vertices and edges in a knowledge graph
that obey logical constraints.Comment: phd thesis, Machine Learning, Natural Language Processing,
Representation Learning, Knowledge Graphs, Entities, Word Embeddings, Entity
Embedding