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A Novel Replica of High Dimensional Data Using Feature Subset Selection Algorithm
Ravichandran M,
Professor (Sr.G),
Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam. ravichandranm@bitsathy.ac.in
Indhumathi K,
PG Scholar,
Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam. indhumathik.se12@bitsathy.ac.in
Priyanka M,
PG Scholar,
Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam. p riyankam. se12@b itsathy. ac.in
Abstract-In the high dimensional data the dimensional reduction is an important factor, for that purpose the clustering based feature subset selection algorithm is proposed in this paper. The features have been clustered according to the class labels. The Relevance of the clustered features has been evaluated. The correlation of the relevant clustered feature is then evaluated. Based on the correlation evaluation the Minimum Spanning Tree (MST) has been generated. The representatives of each class have been identified by the MST. The effectiveness is determined in terms of time required to find the subset of feature and the efficiency is determined terms of quality of the subset. By comparing the proposed algorithm with the existing feature selection algorithms like FCBF, reliefF, CFS etc with respect to the four classification algorithms namely Naive Bayer, the tree based c4.5, the instance based IB1 and rule based RIPPER the proposed algorithm is better in terms of efficiency and accuracy. The results are computed with various types of data set.
A^yworrfs-Minimum Spanning Tree (MST), FCBF, ReliefF, CFS, IBI, Naive Bayes
1. INTRODUCTION
Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. This is since 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 several feature subset selection algorithms, some can effectively remove irrelevant features but fail to handle redundant features yet some of others can eliminate the irrelevant while taking care of the redundant features. The proposed algorithm focuses on the feature subset selection to perform the searching relevant features.
A famous sample 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 unsuccessful at removing redundant features as two predictive but highly correlated features are likely both to be highly weighted. Relief-F[3]
extends Relief, allowing this method to work with noisy and incomplete data sets and to deal with multi-class problems, but still cannot identify redundant features.
2 RELATED WORK
2.1 Fast Correlation-Based Filter
In FCBF [2], from the dataset with N features and a class C the algorithm finds a set of predominant features Sbest for the class concept. Initially it calculates the Symmetric uncertainty (SU) for each feature, selects relevant features into S’list based on the predefined threshold and orders them in descending order according to their SU values. Then the redundant feature has been removed from the S’list. .
According to Heuristic 1 [2], the iteration starts from the first element in S’list and continues as follows. For all the remaining features (from the one right next to fp to the last one in S’list), if fp happens to be a redundant peer to a feature fq, it will be removed from S’list. After one round of filtering features based on fp, the algorithm will take the currently remaining feature right next to fp as the new reference to repeat the filtering process. The algorithm stops until there is no more feature to be removed from
S list.
2.2 Minimum Redundancy Maximum Relevance Feature
The MRMR (minimum redundancy maximum relevance) method [3] selects features that have the highest relevance with the target class and are also minimally redundant, i.e., selects features that are maximally dissimilar to each other. Given gi which represents the features i, and the class label c, their mutual information is defined in terms of their frequencies of appearances p(gi), p(c), and p(gi; c) as follows.
I(gi ’c) = If p(gi ’c)ln p(g ^ dg,dc (1)
p( gi) p(c)
The Maximum-Relevance method selects the top m features in the descent order of I (gi; c), i.e. the best m individual features correlated to the class labels.
max
s
S xI (gi; c) (2)
gie S
Although we can choose the top most features using Maximum-Relevance algorithm, it has been recognized that the m best features are not the best m features, since the correlations among those top features may also be high [11]. In order to remove the redundancy among features, a Minimum-Redundancy criteria is introduced
min7“2 VI(gi ’ gj ) (3)
s |s|2 gj 1
where mutual information between each pair is taken into consideration. The minimum-redundancy maximum relevance (MRMR) feature selection framework combines both optimization criteria of equation 2 and 3.
2.3 Correlation-based Feature
The feature selection was recently proposed as a trade-off between selection process complexity and the need to analyze relationships between features. In this paper, correlation-based modification of the pair wise feature selection. In this paper the results of the experiments in which have compared the correlation-based feature selection strategy with the unmodified pair wise approach. The experiments were performed using neural network classifiers on commonly used benchmark data sets.
2.4. ReliefF
The quality of attributes in the problems with the strong dependencies between the attributes can be estimated with the efficient procedure namely ReliefF [3]. In ReliefF, the feature subset selection is made by means of data preprocessing method. The quality of genes according to their well distinguished values between the instances that are nearer to each other. ReliefF is capable of dealing with multi-class datasets and is an efficient method to deal with noisy and incomplete datasets. It can be used to estimate the quality and identify the existence of conditional dependencies between attributes effectively.
2.5 Support Vector Machine Recursive Feature Elimination
SVM-RFE was introduced by Guyon et. al., for ranking genes from gene expression data for cancer classification [5]. It is now being widely used for gene selection and several improvements have been recently suggested. SVM-RFE, starting with all the genes, removes
the genetic factor that is least significant for classification recursively in a backward elimination manner.
2.6 Fast Binary Feature Selection with Conditional Mutual Information
The Fast Binary Feature Selection [3] is the very fast feature selection technique based on conditional mutual information. The proposed algorithm uses the conditional mutual information to select a family of binary features which are individually discriminating and weekly dependent.
In FBFS, the nai've Bayesian classifier is used in the process of feature selection by means of Conditional Mutual Information Maximization (CMIM).
3 FEATURE SUBSET SELECTION ALGORITHM
3.1 Relevance analysis
The proposed analysis removes irrelevant features by ranking correlation between feature and class SU(X,C) and between feature and feature SU(X,Y) by calculating Symmetrical Uncertainty (SU), given as below,
SU (X ,Y) =
2* Gain( X I Y)
H (X) + H (Y) (1)
Where,
X and Y are the features,
Gain (X IY) = H (X) - H (X IY)
H (X) = - V p (X)log 2 p (x)
xe X
(2)
(3)
H (X I Y) = -V p( y) V p(x I y) log2p(x I y).
yeY xeX
(4)
Here, p (xIy) denotes the posterior probabilities of X given the values of Y and p(x) denotes the prior probabilities for all the values of X.
SU is the personalized version of Information Gain that balances the bias and the SU has the ranges between 0 and 1. If SU between the feature and the class is equal to 1, means that this feature is completely related to the corresponding class. Conversely, if SU between the feature and the class is equal to 0, then the features are irrelevant to this corresponding class.
3.2 Threshold -Relevance feature analysis
As far as the Relevancy analysis is performed, if SU(X, C) is greater than a user determined threshold, we
say that X is a strong Relevance feature and it is given as X e F.
3.3 Redundancy feature analysis
Once the irrelevant features has been removed
based on the threshold value @, the redundancy between the features can be determined by the following condition,
SU(X, Y) < SU(X, C) IISU(X, Y) < SU(Y, C) (5)
In this above equation (5), we can remove the all redundancy feature set and finally we get the reduced dimension.
3.4 Tree Generation and Representative Feature Selection Generate the tree by using the reduced dimension feature, and select the representative of each class label as,
Max (SU(X, C)) (6)
4 EXPERIMENTS AND RESULTS
In order to evaluate the performance of CHD algorithm, 8 different datasets were used. The dataset were taken as image and text type to perform the evaluation. The proposed algorithm CHD is compared with the FCBF.
Data Data Name F I T Domain
1 Chess 37 3196 2 Text
2 mfeat-fourier 77 2000 10 Image
3 fbis.wc 2001 2463 17 Text
4 tr12.wc 5805 313 8 Text
5 tr23.wc 5833 204 6 Text
6 tr11.wc 6430 414 9 Text
7 PIX10P 10001 100 10 Image
8 ORL10P 10305 100 10 Image
4.1 Classification Accuracy
The 10-fold cross-validation precisions of the four different types of classifiers on the 8 data sets before and after each feature selection algorithm is performed, respectively.
4.1.1 Naive Bayes
The Naive Bayes classifier, the classification decision may often be perfect even if its probability estimates are inaccurate. Even though, Naive Bayes classifier is simple, it can often outperform more complex classification methods.
4.1.2 C4.5
In C4.5 classifier,the user defined threshold can splits the attribute values into two partitions. The values above the threshold can be partitioned into one child and
Algorithm: CHD
Input: S(f1,f2,..., fn, C) //training dataset
Th //threshold value
Output: I //Feature subset
Begin:
I=empty;
for i=1 to N begin
SUic =calculateSU(fi ,C ); if(SUic >Th) addto(I, fi);
end
I=Desc (I) based on SUic;
count=0;
endcount=-1;
flag=0;
while endcount<>count begin count=endcount; fp =firstelement(I);
while fp <>NULL AND flag<>1 begin fq =lastelement (I); pass=0;
while fq <>NULL AND pass <>1 begin if(fp == fq) break; tempSUpq = calculateSU(fp , fq) if(tempSUpq>SUp && tempSUpq>SUq)
delete(I, fq ); pass=1;
count=count+1; else fq =previous(I, fq ); end
fp =next(I, fp ); end
end
MinimumSpanTree(SU(fi,C)); for each C in S repc=Max(SU(fi,C);
Data Name Classification precision of Naive Bayes with
CHD FCBF
Chess 92.98 92.09
mfeat-fourier 80.25 79.20
fbis.wc 70.21 52.25
tr12.wc 84.25 57.95
tr23.wc 94.11 53.98
tr11.wc 82.47 58.72
PIX10P 98.00 98.40
ORL10P 99.20 98.80
the remaining as another child. The missing attribute values can also be handled by this algorithm.
In pseudo code the algorithm is:
1. Check for base cases
2. For each attribute a (Find the normalized information gain from splitting on a)
3. Let a_best be the attribute with the highest normalized information gain
4. Create a decision node that splits on a_best
5. Recur on the sublists obtained by splitting on a_best, and add those nodes as children of node
Data Name Classification precision of C4.5 with
CHD FCBF
Chess 94.12 94.12
mfeat-fourier 71.25 75.74
fbis.wc 81.68 85.43
tr12.wc 89.87 94.80
tr23.wc 80.22 82.04
tr11.wc 82.47 58.72
PIX10P 97.00 95.40
ORL10P 90.33 82.60
4.1.3 IB1
IBL Streams is an instance-based learning algorithm for performing the classification and regression on data streams. The method is capable to handle large streams through low requirements in terms of memory and computational control. Moreover, it disposes of apparatuses for adapting to concept drift and concept shift. The implementation of IBL Streams is supposed to be used as an extension to the MOA (Massive Online Analysis) framework for data stream mining.
Data Name Classification precision of IB1 with
CHD FCBF
Chess 90.18 91.47
mfeat-fourier 77.87 81.69
fbis.wc 60.09 61.91
tr12.wc 82.11 83.43
tr23.wc 90.18 86.55
tr11.wc 78.43 79.65
PIX10P 99.00 99.00
ORL10P 100.00 97.60
4.1.4 RIPPER
RIPPER, is an inductive rule apprentice. This algorithm generated a detection perfect composed of resource rules that was built to detect future examples of malicious executable. This algorithm used libBFD evidence as features. By RIPPER, this is a rule-based learner. The RIPPER builds a set of rules that identify the classes though
it minimizing the amount of error on it. The error is distinct by the number of exercise examples misclassified by the rules.
Data Name Classification precision of RIPPER with
CFSS FCBF
Chess 94.09 94.09
mfeat-fourier 70.40 73.46
fbis.wc 65.58 68.18
tr12.wc 82.53 81.13
tr23.wc 91.15 95.96
tr11.wc 80.13 79.52
PIX10P 96.67 93.00
ORL10P 85.33 73.80
4.2 Sensitivity Analysis
Like many other feature selection algorithms, our proposed CHD also requires a parameter 6 that is the threshold of feature relevance. Different 6 values might end with different classification results. In order to explore which parameter value results in the best classification accuracy for a specific classification problem with a certain classifier, a 10 fold cross-validation strategy stood employed to reveal how the classification accuracy is changing with value of the parameter6 .
CFSS
■ ISIAl'Vt BAYtS HCa.D BlBl ■ KIPPbK
FCBF
120
100
■ NAIVb BAYbS HC4.b ■ IB1 ■ KIPPtK
5 CONCLUSION
In this paper, we have presented a novel clustering-based feature subset selection algorithm for high dimensional data. The algorithm contains 1) removing irrelevant features, 2) partitioning the MST and 3) selecting representative features. In the proposed algorithm, a cluster contains of features. Each cluster is preserved as a single feature and thus dimensionality is extremely reduced.
The results had illustrated that the proposed method is very effective and has great potential for relevant selection.
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