6,075 research outputs found
Change detection in categorical evolving data streams
Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution.
To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
Missing Value Imputation With Unsupervised Backpropagation
Many data mining and data analysis techniques operate on dense matrices or
complete tables of data. Real-world data sets, however, often contain unknown
values. Even many classification algorithms that are designed to operate with
missing values still exhibit deteriorated accuracy. One approach to handling
missing values is to fill in (impute) the missing values. In this paper, we
present a technique for unsupervised learning called Unsupervised
Backpropagation (UBP), which trains a multi-layer perceptron to fit to the
manifold sampled by a set of observed point-vectors. We evaluate UBP with the
task of imputing missing values in datasets, and show that UBP is able to
predict missing values with significantly lower sum-squared error than other
collaborative filtering and imputation techniques. We also demonstrate with 24
datasets and 9 supervised learning algorithms that classification accuracy is
usually higher when randomly-withheld values are imputed using UBP, rather than
with other methods
PRESISTANT: Learning based assistant for data pre-processing
Data pre-processing is one of the most time consuming and relevant steps in a
data analysis process (e.g., classification task). A given data pre-processing
operator (e.g., transformation) can have positive, negative or zero impact on
the final result of the analysis. Expert users have the required knowledge to
find the right pre-processing operators. However, when it comes to non-experts,
they are overwhelmed by the amount of pre-processing operators and it is
challenging for them to find operators that would positively impact their
analysis (e.g., increase the predictive accuracy of a classifier). Existing
solutions either assume that users have expert knowledge, or they recommend
pre-processing operators that are only "syntactically" applicable to a dataset,
without taking into account their impact on the final analysis. In this work,
we aim at providing assistance to non-expert users by recommending data
pre-processing operators that are ranked according to their impact on the final
analysis. We developed a tool PRESISTANT, that uses Random Forests to learn the
impact of pre-processing operators on the performance (e.g., predictive
accuracy) of 5 different classification algorithms, such as J48, Naive Bayes,
PART, Logistic Regression, and Nearest Neighbor. Extensive evaluations on the
recommendations provided by our tool, show that PRESISTANT can effectively help
non-experts in order to achieve improved results in their analytical tasks
A Semi-Supervised Approach to the Detection and Characterization of Outliers in Categorical Data
International audienceIn this paper we introduce a new approach of semi-supervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationships among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model characterizing the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semi-supervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposal produces a discriminative model that can be easily interpreted and used for the exploration of the data
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler
Hierarchical temporal memory (HTM) is an emerging machine learning algorithm,
with the potential to provide a means to perform predictions on spatiotemporal
data. The algorithm, inspired by the neocortex, currently does not have a
comprehensive mathematical framework. This work brings together all aspects of
the spatial pooler (SP), a critical learning component in HTM, under a single
unifying framework. The primary learning mechanism is explored, where a maximum
likelihood estimator for determining the degree of permanence update is
proposed. The boosting mechanisms are studied and found to be only relevant
during the initial few iterations of the network. Observations are made
relating HTM to well-known algorithms such as competitive learning and
attribute bagging. Methods are provided for using the SP for classification as
well as dimensionality reduction. Empirical evidence verifies that given the
proper parameterizations, the SP may be used for feature learning.Comment: This work was submitted for publication and is currently under
review. For associated code, see https://github.com/tehtechguy/mHT
A space-structure based dissimilarity measure for categorical data
The development of analysis methods for categorical data begun in 90's decade, and it has been booming in the last years. On the other hand, the performance of many of these methods depends on the used metric. Therefore, determining a dissimilarity measure for categorical data is one of the most attractive and recent challenges in data mining problems. However, several similarity/dissimilarity measures proposed in the literature have drawbacks due to high computational cost, or poor performance. For this reason, we propose a new distance metric for categorical data. We call it: Weighted pairing (W-P) based on feature space-structure, where the weights are understood like a degree of contribution of an attribute to the compact cluster structure. The performance of W-P metric was evaluated in the unsupervised learning framework in terms of cluster quality index. We test the W-P in six real categorical datasets downloaded from the public UCI repository, and we make a comparison with the distance metric (DM3) method and hamming metric (H-SBI). Results show that our proposal outperforms DM3 and H-SBI in different experimental configurations. Also, the W-P achieves highest rand index values and a better clustering discriminant than the other methods
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