在处理不平衡数据集时，可以在特征选择之前或之后使用过采样技术。以下是一种解决方案，其中包含代码示例：

1. 特征选择之前使用过采样技术：

from imblearn.over_sampling import SMOTE
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 假设X是特征矩阵，y是目标变量
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 使用SMOTE过采样技术生成合成样本
smote = SMOTE()
X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)

# 特征选择
k_best = SelectKBest(chi2, k=10)
X_train_selected = k_best.fit_transform(X_train_resampled, y_train_resampled)
X_test_selected = k_best.transform(X_test)

# 构建模型并进行训练和预测
model = LogisticRegression()
model.fit(X_train_selected, y_train_resampled)
y_pred = model.predict(X_test_selected)

# 评估模型性能
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

2. 特征选择之后使用过采样技术：

from imblearn.over_sampling import SMOTE
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 假设X是特征矩阵，y是目标变量
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 特征选择
k_best = SelectKBest(chi2, k=10)
X_train_selected = k_best.fit_transform(X_train, y_train)
X_test_selected = k_best.transform(X_test)

# 使用SMOTE过采样技术生成合成样本
smote = SMOTE()
X_train_resampled, y_train_resampled = smote.fit_resample(X_train_selected, y_train)

# 构建模型并进行训练和预测
model = LogisticRegression()
model.fit(X_train_resampled, y_train_resampled)
y_pred = model.predict(X_test_selected)

# 评估模型性能
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

无论是在特征选择之前还是之后使用过采样技术，都需要注意过采样可能导致过拟合问题。因此，在使用过采样技术之前，可以尝试其他方法来处理数据不平衡问题，例如欠采样、集成方法等。另外，特征选择也可以使用其他方法，如基于模型的选择、互信息等。具体选择哪种方法需要根据实际情况进行评估和比较。