Abstract: Human beings have the ability to make logical
decisions. Although human decision - making is often optimal, it is
insufficient when huge amount of data is to be classified. Medical
dataset is a vital ingredient used in predicting patient’s health
condition. In other to have the best prediction, there calls for most
suitable machine learning algorithms. This work compared the
performance of Artificial Neural Network (ANN) and Decision Tree
Algorithms (DTA) as regards to some performance metrics using
diabetes data. WEKA software was used for the implementation of
the algorithms. Multilayer Perceptron (MLP) and Radial Basis
Function (RBF) were the two algorithms used for ANN, while
RegTree and LADTree algorithms were the DTA models used. From
the results obtained, DTA performed better than ANN. The Root
Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is
0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206
respectively.
Abstract: This study investigates the use of a time-series of
MODIS NDVI data to identify agricultural land cover change on an
annual time step (2007 - 2012) and characterize the trend. Following
an ISODATA classification of the MODIS imagery to selectively
mask areas not agriculture or semi-natural, NDVI signatures were
created to identify areas cereals and vineyards with the aid of
ancillary, pictometry and field sample data for 2010. The NDVI
signature curve and training samples were used to create a decision
tree model in WEKA 3.6.9 using decision tree classifier (J48)
algorithm; Model 1 including ISODATA classification and Model 2
not. These two models were then used to classify all data for the
study area for 2010, producing land cover maps with classification
accuracies of 77% and 80% for Model 1 and 2 respectively. Model 2
was subsequently used to create land cover classification and change
detection maps for all other years. Subtle changes and areas of
consistency (unchanged) were observed in the agricultural classes
and crop practices. Over the years as predicted by the land cover
classification. Forty one percent of the catchment comprised of
cereals with 35% possibly following a crop rotation system.
Vineyards largely remained constant with only one percent
conversion to vineyard from other land cover classes.
Abstract: As the number of networked computers grows,
intrusion detection is an essential component in keeping networks
secure. Various approaches for intrusion detection are currently
being in use with each one has its own merits and demerits. This
paper presents our work to test and improve the performance of a
new class of decision tree c-fuzzy decision tree to detect intrusion.
The work also includes identifying best candidate feature sub set to
build the efficient c-fuzzy decision tree based Intrusion Detection
System (IDS). We investigated the usefulness of c-fuzzy decision
tree for developing IDS with a data partition based on horizontal
fragmentation. Empirical results indicate the usefulness of our
approach in developing the efficient IDS.
Abstract: Scene interpretation systems need to match (often ambiguous)
low-level input data to concepts from a high-level ontology.
In many domains, these decisions are uncertain and benefit greatly
from proper context. This paper demonstrates the use of decision
trees for estimating class probabilities for regions described by feature
vectors, and shows how context can be introduced in order to improve
the matching performance.
Abstract: Keystroke authentication is a new access control system
to identify legitimate users via their typing behavior. In this paper,
machine learning techniques are adapted for keystroke authentication.
Seven learning methods are used to build models to differentiate user
keystroke patterns. The selected classification methods are Decision
Tree, Naive Bayesian, Instance Based Learning, Decision Table, One
Rule, Random Tree and K-star. Among these methods, three of them
are studied in more details. The results show that machine learning
is a feasible alternative for keystroke authentication. Compared to
the conventional Nearest Neighbour method in the recent research,
learning methods especially Decision Tree can be more accurate. In
addition, the experiment results reveal that 3-Grams is more accurate
than 2-Grams and 4-Grams for feature extraction. Also, combination
of attributes tend to result higher accuracy.
Abstract: The next stage of the home networking environment is
supposed to be ubiquitous, where each piece of material is equipped
with an RFID (Radio Frequency Identification) tag. To fully support
the ubiquitous environment, home networking middleware should be
able to recommend home services based on a user-s interests and
efficiently manage information on service usage profiles for the users.
Therefore, USN (Ubiquitous Sensor Network) technology, which
recognizes and manages a appliance-s state-information (location,
capabilities, and so on) by connecting RFID tags is considered. The
Intelligent Multi-Agent Middleware (IMAM) architecture was
proposed to intelligently manage the mobile RFID-based home
networking and to automatically supply information about home
services that match a user-s interests. Evaluation results for
personalization services for IMAM using Bayesian-Net and Decision
Trees are presented.
Abstract: This paper investigates the issue of building decision
trees from data with imprecise class values where imprecision is
encoded in the form of possibility distributions. The Information
Affinity similarity measure is introduced into the well-known gain
ratio criterion in order to assess the homogeneity of a set of
possibility distributions representing instances-s classes belonging to
a given training partition. For the experimental study, we proposed an
information affinity based performance criterion which we have used
in order to show the performance of the approach on well-known
benchmarks.