15,200 research outputs found
A System for Induction of Oblique Decision Trees
This article describes a new system for induction of oblique decision trees.
This system, OC1, combines deterministic hill-climbing with two forms of
randomization to find a good oblique split (in the form of a hyperplane) at
each node of a decision tree. Oblique decision tree methods are tuned
especially for domains in which the attributes are numeric, although they can
be adapted to symbolic or mixed symbolic/numeric attributes. We present
extensive empirical studies, using both real and artificial data, that analyze
OC1's ability to construct oblique trees that are smaller and more accurate
than their axis-parallel counterparts. We also examine the benefits of
randomization for the construction of oblique decision trees.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
A Survey of Parallel Data Mining
With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons
learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms
Data Engineering for the Analysis of Semiconductor Manufacturing Data
We have analyzed manufacturing data from several different semiconductor
manufacturing plants, using decision tree induction software called
Q-YIELD. The software generates rules for predicting when a given product
should be rejected. The rules are intended to help the process engineers
improve the yield of the product, by helping them to discover the causes
of rejection. Experience with Q-YIELD has taught us the importance of
data engineering -- preprocessing the data to enable or facilitate
decision tree induction. This paper discusses some of the data engineering
problems we have encountered with semiconductor manufacturing data.
The paper deals with two broad classes of problems: engineering the features
in a feature vector representation and engineering the definition of the
target concept (the classes). Manufacturing process data present special
problems for feature engineering, since the data have multiple levels of
granularity (detail, resolution). Engineering the target concept is important,
due to our focus on understanding the past, as opposed to the more common
focus in machine learning on predicting the future
Decision Making in the Medical Domain: Comparing the Effectiveness of GP-Generated Fuzzy Intelligent Structures
ABSTRACT: In this work, we examine the effectiveness of two intelligent models in medical domains. Namely, we apply grammar-guided genetic programming to produce fuzzy intelligent structures, such as fuzzy rule-based systems and fuzzy Petri nets, in medical data mining tasks. First, we use two context-free grammars to describe fuzzy rule-based systems and fuzzy Petri nets with genetic programming. Then, we apply cellular encoding in order to express the fuzzy Petri nets with arbitrary size and topology. The models are examined thoroughly in four real-world medical data sets. Results are presented in detail and the competitive advantages and drawbacks of the selected methodologies are discussed, in respect to the nature of each application domain. Conclusions are drawn on the effectiveness and efficiency of the presented approach
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
This paper introduces ICET, a new algorithm for cost-sensitive
classification. ICET uses a genetic algorithm to evolve a population of biases
for a decision tree induction algorithm. The fitness function of the genetic
algorithm is the average cost of classification when using the decision tree,
including both the costs of tests (features, measurements) and the costs of
classification errors. ICET is compared here with three other algorithms for
cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5,
which classifies without regard to cost. The five algorithms are evaluated
empirically on five real-world medical datasets. Three sets of experiments are
performed. The first set examines the baseline performance of the five
algorithms on the five datasets and establishes that ICET performs
significantly better than its competitors. The second set tests the robustness
of ICET under a variety of conditions and shows that ICET maintains its
advantage. The third set looks at ICET's search in bias space and discovers a
way to improve the search.Comment: See http://www.jair.org/ for any accompanying file
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data
COMET is a single-pass MapReduce algorithm for learning on large-scale data.
It builds multiple random forest ensembles on distributed blocks of data and
merges them into a mega-ensemble. This approach is appropriate when learning
from massive-scale data that is too large to fit on a single machine. To get
the best accuracy, IVoting should be used instead of bagging to generate the
training subset for each decision tree in the random forest. Experiments with
two large datasets (5GB and 50GB compressed) show that COMET compares favorably
(in both accuracy and training time) to learning on a subsample of data using a
serial algorithm. Finally, we propose a new Gaussian approach for lazy ensemble
evaluation which dynamically decides how many ensemble members to evaluate per
data point; this can reduce evaluation cost by 100X or more
Porting Decision Tree Algorithms to Multicore using FastFlow
The whole computer hardware industry embraced multicores. For these machines,
the extreme optimisation of sequential algorithms is no longer sufficient to
squeeze the real machine power, which can be only exploited via thread-level
parallelism. Decision tree algorithms exhibit natural concurrency that makes
them suitable to be parallelised. This paper presents an approach for
easy-yet-efficient porting of an implementation of the C4.5 algorithm on
multicores. The parallel porting requires minimal changes to the original
sequential code, and it is able to exploit up to 7X speedup on an Intel
dual-quad core machine.Comment: 18 pages + cove
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