# Interclass Interference Suppression in Multi-Class Problems
## 4 Design of Interclass Interference Suppression Algorithms
### Construction of Binary Classifier
\(i=i+1\); endwhile \(aveNum\_Redu\gets\underset{i}{\text{\emph{sum}}}(iNum\_Redu)/M\)// the average number of attributes \(used\_Redu\gets\underset{i}{\text{\emph{val}}}(iRudu\)// used feature subset for M-class problem \(Num\gets\underset{i}{\text{\emph{sum}}}(iNum)\)// the number of rules for M-class problem \(aveSupp\gets\underset{i}{\text{\emph{sum}}}(iSum\_Supp)/Num\)// average support coefficient of rules \(aveLen\gets\underset{i}{\text{\emph{sum}}}(iSum\_Len)/Num\)// average rule length \(\overline{Num}\leftarrow\underset{i}{\text{\emph{sum}}}(i\overline{Num})\)// the number of rules for negative class \(aveSupp\gets\underset{i}{\text{\emph{sum}}}(\overline{iSum\_Supp})/ \overline{Num}\)// average support coefficient of negative class \(aveLen\gets\underset{i}{\text{\emph{sum}}}(\overline{iSum\_Len})/ \overline{Num}\)// average rule length for negative class
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**Algorithm 1** Binary classifier construction procedure based on OVA strategy
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Input: the original dataset Output:\(M(M-1)/2\) binary classifiers; the corresponding feature subset \(ijRedu\); rule set \(ijRuleset\); support coefficient \(ijSupp\) of M binary classifiers; the average number of attributes \(aveNum\_Redu\); the overall feature subsets \(used\_Redu\); the equivalent overall number of rules \(Num\); average support coefficient \(aveSupp\); average rule length \(aveLen\). Begin for each class \(i\) in the original class problem (\(1\leq i\leq M-1\)) for each class \(i\) in the original class problem (\(i+1\leq j\leq M\)) 1. Search all instances of class \(i\) and class \(j\) in the original dataset and construct new dataset \(ij\_Dataset\); 2. Perform feature selection for dataset \(ij\_Dataset\) through rough set, obtain the feature subset \(ijRedu\) and denote the number of attributes in feature subset \(ijRedu\) as \(ijNum\_Redu\). 3. Extract rules and obtain the rule set \(ijRuleset\) and calculate the rule's support set \(ijSupp\). 4. For class \(i\), calculate the rule number \(iNum\), the sum of the rule support \(iSum\_Supp\) and the sum of the rule length \(iSum\_Len\). Then store \(iNum\), \(iSum\_Supp\) and \(iSum\_Len\) in the hash table \(Sum\_Num\), \(Sum\_Supp\) and \(Sum\_Len\) respectively with the key being class \(i\). 5. For class \(j\), calculate the rule number \(jNum\), the sum of support coefficient \(jSum\_Supp\) and the sum of rule length \(jSum\_Len\). Then store \(jNum\), \(jSum\_Supp\), \(jSum\_Len\) in the above hash table \(Sum\_Num\), \(Sum\_Supp\) and \(Sum\_Len\) respectively with the key being class \(j\). endfor endfor \(aveNum\_Redu\gets sum\_{i,j}(ijNum\_Redu)\)\((M(M-1)/2)\). \(used\_Redu\leftarrow\underset{ij}{\text{\emph{J}}}\)\(ijRedu\). // actually used reduction subset \(Num\leftarrow\underset{i}{\text{\emph{Sum}}}\)\((Sum\_Num\)\((M-1))\). // equivalent overall rule number \(aveSupp\gets sum\_{i}(Sum\_Supp)\underset{i}{\text{\emph{Sum}}}\)\((Sum\_Num)\). // average support coefficient \(aveLen\gets sum\underset{i}{\text{\emph{Sum}}}\)\((Sum\_Len)\underset{i}{\text{\emph{Sum}}}\)\((Sum\_Num)\). // average rule length End
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**Algorithm 2** Binary classifier construction procedure based on OVO strategy