## Materials and Methods
### Methods
#### 2.2.1 Outline of the Proposed Automatic Classification
\begin{table}
\begin{tabular}{l l} \hline \hline
1 BayesNet & 27 NNge (Non-Nested generalized exemplars) \\
2 DMNBtext (Discriminative Multinomial Naive Bayes) & 28 OneR (1-R classifier) \\
3 NaiveBayes & 29 PART (Partial decision trees) \\
4 NaiveBayesMultinomial & 30 Ridor (Rlpple-DOwn Rule) \\
5 NaiveBayesMultinomialUpdateable & 31 ZeroR (0-R classifier) \\
6 NaiveBayesSimple & 32 BFTree (Best-First decision tree) \\
7 NaiveBayesUpdateable & 33 DecisionStump \\
8 B1 (Instance-based classifier) & 34 FT (Functional Trees) \\
9 KStar & 35 J48 (C4.5 Decision Tree) \\
10 LWL (Locally Weighted Learning) & 36 J48 graft (Decision Tree Grafting) \\
11 AdaBoostM1 & 37 LADTree (LogitBoost Alternating Decision Tree ) \\
12 AttributeSelectedClassifier & 38 RandomForest \\
13 Bagging & 39 RandomTree \\
14 ClassificationViaClustering & 40 REPTree (Reduced-Error Pruning) \\
15 ClassificationViaRegression & 41 SimpleCart \\
16 CVParameterSelection (Cross-Validation) & 42 Logistic \\
17 END (Ensembles of Balanced Nested Dichotomies) & 43 MultilayerPerceptron \\
18 FilteredClassifier & 44 RBFNetwork (Radial Basis Function Network) \\
19 Grading & 45 SimpleLogistic \\
20 LogitBoost (Additive Logistic Regression) & 46 SMO (Sequential Minimal Optimization) \\
21 MultiBoostAB (Ada Boost) & 47 DTNB (Decision Table/Naive Bayes hybrid) \\
22 MultiClassClassifier & 48 Dagging \\
23 MultiScheme & 49 Decorate (Diverse Ensemble Creation) \\
24 HyperPieps & 50 LMT (Logistic Model Trees) \\
25 VFI (Voting Feature Intervals) & 51 NBTree (Naive-Bayes Tree hybrid) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Subset of classification algorithms \(\{a_{1}\}\subseteq A\). _Architectures_. The set \(Q\) is composed of binary and multiclass architectures used in the classification. In this way, \(q_{k}\) represents a classification architecture, where \(q_{k}\subset Q\), and \(k\) is the index of the current architecture. Table 4 summarizes the algorithms from sets \(q_{1}\) and \(q_{2}\). As depicted in Figure 4, phase 1 is divided into 5 activities. The first 4 activities are repeated as many times as the number of the required classification models, \(m_{n}\). In the activity number 5, the best element of the set \(M\) is selected. These activities are described below:  
1. From the literature, groups of features that can be extracted from Q-band chromosome images where formed. These were listed as the subset of features \(c_{1}\), \(c_{2}\), \(c_{3}\), and \(c_{4}\) and are presented in Table 1. In this activity, one of the feature groups \(c_{i}\) is selected to be extracted from every chromosome in the image database. With these features, two sets are generated in separated datasets to be utilized in training. The first dataset is used to train the classifiers and the second one as test dataset. 2. In this activity, a group of classification algorithms available in the Weka platform is selected and identified as the set \(A\). These elements conform the subset \(a_{1}\). According to the experimental results, the elements of the subset \(a_{1}\) were modified to form the subset of algorithms, \(a_{2}\). The algorithms of subset \(a_{1}\) are enlisted in Table 2, and for subset \(a_{2}\) in Table 3.
3. This activity defines the architectures that will be used in chromosomes classification. An architecture \(q_{n}\) represents how the chromosomes are going to be classified in one of the 24 output classes. For example, one architecture could be a multiclass classification, where the chromosome would be assigned directly to 1 of the 24 classes. Another option is to divide the group into autosome and sex chromosomes, and identify if it belongs to one of this groups or not (binary classification). Table 4 summarizes the defined architectures for this activity. 4. Training and testing of a model \(m_{n}\) is performed in this activity. The training and testing dataset, the set of algorithms \(a_{j}\) and the classification architecture \(q_{k}\) are the elements of the current model, \(m_{n}\).