Iaetsd an efficient and large data base using subset selection algorithm

An Efficient and Large data base using Subset Selection Algorithm
for Multidimensional Data Extraction
M.Nagendra
M.Tech, Dept. of CSE, Audisankara College of Engineering & Technology, Gudur, A.P, India
Abstract- Feature subset selection is an effective way for reducing dimensionality, removing irrelevant
data, increasing learning accuracy and improving results comprehensibility. This process improved by
cluster based FAST Algorithm and Fuzzy Logic. FAST Algorithm can be used to Identify and removing
the irrelevant data set. This algorithm process implements using two different steps that is graph theoretic
clustering methods and representative feature cluster is selected. Feature subset selection research has
focused on searching for relevant features. The proposed fuzzy logic has focused on minimized redundant
data set and improves the feature subset accuracy.The FAST algorithm works in two steps. In the first
step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the
most representative feature that is strongly related to target classes is selected from each cluster to form a
subset of features. Features in different clusters are relatively independent; the clustering-based strategy
of FAST has a high probability of producing a subset of useful and independent features. To ensure the
efficiency of FAST, we adopt the efficient minimum-spanning tree clustering method.
Index Terms – Feature Extraction, Minimum Spanning Tree, Clustering
I. INTRODUCTION
The performance, robustness, and usefulness of classification algorithms are improved when relatively
few features are involved in the classification. Thus, selecting relevant features for the construction of
classifiers has received a great deal of attention.With the aim of choosing a subset of good features
with respect to the target concepts, feature subset selection is an effective way for reducing
dimensionality, removing irrelevant data, increasing learning accuracy, and improving result
comprehensibility. Many feature subset selection methods have been proposed and studied for
machine learning applications.
i. Existing System
The embedded methods incorporate feature selection as a part of the training process and are usually
specific to given learning algorithms, and therefore may be more efficient than the other three categories.
Traditional machine learning algorithms like decision trees or artificial neural networks are examples of
Proceedings of International Conference on Developments in Engineering Research
ISBN NO : 378 - 26 - 13840 - 9
www.iaetsd.in
INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT
129

embedded approaches. The wrapper methods use the predictive accuracy of a predetermined learning
algorithm to determine the goodness of the selected subsets, the accuracy of the learning algorithms is
usually high. However, the generality of the selected features is limited and the computational complexity
is large. The filter methods are independent of learning algorithms, with good generality. Their
computational complexity is low, but the accuracy of the learning algorithms is not guaranteed. The
hybrid methods are a combination of filter and wrapper methods by using a filter method to reduce search
space that will be considered by the subsequent wrapper. They mainly focus on combining filter and
wrapper methods to achieve the best possible performance with a particular learning algorithm with
similar time complexity of the filter methods.
ii. Proposed System
Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and
redundant features as possible. This is because irrelevant features do not contribute to the predictive
accuracy and redundant features do not redound to getting a better predictor for that they provide mostly
information which is already present in other feature(s). Of the many feature subset selection algorithms,
some can effectively eliminate irrelevant features but fail to handle redundant features yet some of others
can eliminate the irrelevant while taking care of the redundant features. Our proposed FAST algorithm
falls into the second group. Traditionally, feature subset selection research has focused on searching for
relevant features. A well-known example is Relief which weighs each feature according to its ability to
discriminate instances under different targets based on distance-based criteria function. However, Relief
is ineffective at removing redundant features as two predictive but highly correlated features are likely
both to be highly weighted. Relief-F extends Relief, enabling this method to work with noisy and
incomplete data sets and to deal with multiclass problems, but still cannot identify redundant features.
iii.Framework of Feature Extraction
ISBN NO : 378 - 26 - 13840 - 9
www.iaetsd.in
130

Fig: Framework of Feature Cluster-Based Extraction Algorithm
II. FEATURE CLUSTER BASED EXTRACTION ALGORITHM
Irrelevant features, along with redundant features, severely affect the accuracy of the learning machines.
Thus, feature subset selection should be able to identify and remove as much of the irrelevant and
redundant information as possible. Moreover, “good feature subsets contain features highly correlated
with the class, yet uncorrelated with each other.” Keeping these in mind, we develop a novel algorithm
which can efficiently and effectively deal with both irrelevant and redundant features, and obtain a good
feature subset. We achieve this through a new feature selection framework which composed of the two
connected components of irrelevant feature removal and redundant feature elimination. The irrelevant
feature removal is straightforward once the right relevance measure is defined or selected, while the
redundant feature elimination is a bit of sophisticated.
In our proposed FAST algorithm, it involves (i) the construction of the minimum spanning tree (MST)
from a weighted complete graph; (ii) the partitioning of the MST into a forest with each tree representing
a cluster; and (iii) the selection of representative features from the clusters. Feature subset selection can
be the process that identifies and retains the strong T-Relevance features and selects R-Features from
feature clusters. The behind heuristics are that
1) Irrelevant features have no/weak correlation with target concept;
2) Redundant features are assembled in a cluster and a representative feature can be taken out of the
cluster.
III. ALGORITHM AND TIME COMPLEXITY ANALYSIS
The proposed FAST algorithm logically consists of three steps: (i) removing irrelevant features, (ii)
constructing a MST from relative ones, and (iii) partitioning the MST and selecting representative
features.
i. Algorithm: FAST
Inputs: ( , , … , , ) - the given data set - the − ℎ ℎ .
Output: − .
1 = 1
2 − = ( , )
3 − > ℎ
4 = ∪ { };
5 = ;
6 ℎ , ⊂
7 − = ,
ISBN NO : 378 - 26 - 13840 - 9
www.iaetsd.in
131

8 ℎ
ℎ ℎ ℎ
9 = ( );
10 =
11 ℎ ∈
12 , < ( , ) ∧ , < , ℎ
13 = −
14 = ∅
15 ℎ ∈
16 = ∈
( , )
17 = ∪ ;
18
ii. Time Complexity
The major amount of work for Algorithm 1 involves the computation of SU values for T-
Relevance and F-Correlation, which has linear complexity in terms of the number of instances in a given
data set.
The first part of the algorithm has a linear time complexity O(m) in terms of the number of
features m. Assuming k(1≤ k ≤ m) features are selected as relevant ones in the first part, when k = 1, only
on feature is selected. The second part of the algorithm firstly constructs a complete graph from relevant
features and the complexity is O(k2
), and then generates a MST from the graph using Prim Algorithm
whose time complexity is O(k2
). The third part partitions the MST and Chooses the representative features
with the complexity of O(k). Thus when 1<k≤ m, the complexity of the Algorithm is O(m)
CONCLUSION
This project presented a novel clustering – based feature extraction algorithm for high
dimensional data. The algorithm involves 1) removing irrelevant features, 2) constructing a minimum
spanning tree from relative ones, and 3) partitioning the MST and extracting representative features.
The purpose of cluster analysis has been established to be more effective than feature extraction
algorithms. Since high dimensionality and accuracy are the two major concerns of clustering, we have
considered them together in this paper for the finer cluster for removing the irrelevant and redundant
features. The proposed supervised clustering algorithm is processed for high dimensional data to improve
the accuracy and check the probability of the patterns. Retrieval of relevant data should be faster and
more accurate. This displays results based on the high probability density thereby reducing the
dimensionality of the data.
FUTURE ENHANCEMENT
ISBN NO : 378 - 26 - 13840 - 9
www.iaetsd.in
132

In the near feature, we plan to analyze the distinct types of relationship measures and some
formal properties of feature space.
REFERENCES
[1] Almuallim H. and Dietterich T.G., Algorithms for Identifying Relevant Features, In Proceedings of
the 9th Canadian Conference on AI, pp 38-45, 1992.
[2] Arauzo-Azofra A., Benitez J.M. and Castro J.L., A feature set measure based on relief, In Proceedings
of the fifth international conference on Recent Advances in Soft Computing, pp 104-109, 2004.
[3] Bell D.A. and Wang, H., A formalism for relevance and its application in feature subset selection,
Machine Learning, 41(2), pp 175-195, 2000.
[4] Biesiada J. and Duch W., Features election for high-dimensionaldatała Pearson redundancy based
filter, AdvancesinSoftComputing, 45, pp 242C249, 2008.
[5] Butterworth R., Piatetsky-Shapiro G. and Simovici D.A., On Feature Selection through Clustering, In
Proceedings of the Fifth IEEE international Conference on Data Mining, pp 581-584, 2005.
[6] Cardie, C., Using decision trees to improve case-based learning, In Proceedings of Tenth International
Conference on Machine Learning, pp 25-32, 1993.
[7] Butterworth R., Piatetsky-Shapiro G. and Simovici D.A., On Feature Selection through Clustering, In
Proceedings of the Fifth IEEE international Conference on Data Mining, pp 581-584, 2005.
[8] Cardie, C., Using decision trees to improve case-based learning, In Proceedings of Tenth International
Conference on Machine Learning, pp 25-32, 1993.
AUTHORS
MosaNagendrahas received his B.Tech degree in Information Technology from
Jagan’s College of Engineering & Technology, Venkatachalam affiliated to JNTU,
Anantapur in 2012 and pursuing M.Tech degree in Computer Science & Engineering
at Audisankara College of Engineering & Technology, Gudur affiliated to JNTU,
Anantapur in (2012-2014).
ISBN NO : 378 - 26 - 13840 - 9
www.iaetsd.in
133

Iaetsd an efficient and large data base using subset selection algorithm

Recommended

More Related Content

What's hot (16)

Viewers also liked (6)

Similar to Iaetsd an efficient and large data base using subset selection algorithm (20)

More from Iaetsd Iaetsd (20)

Recently uploaded (20)

Iaetsd an efficient and large data base using subset selection algorithm